research-article

Anomaly detection: A survey

Authors:

Varun Chandola,

Arindam Banerjee,

Vipin KumarAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 41, Issue 3

Article No.: 15, Pages 1 - 58

https://doi.org/10.1145/1541880.1541882

Published: 30 July 2009 Publication History

Abstract

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

References

[1]

Abe, N., Zadrozny, B., and Langford, J. 2006. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 504--509.

Digital Library

[2]

Abraham, B. and Box, G. E. P. 1979. Bayesian analysis of some outlier problems in time series. Biometrika 66, 2, 229--236.

[3]

Abraham, B. and Chuang, A. 1989. Outlier detection and time series modeling. Technometrics 31, 2, 241--248.

Digital Library

[4]

Addison, J., Wermter, S., and MacIntyre, J. 1999. Effectiveness of feature extraction in neural network architectures for novelty detection. In Proceedings of the 9th International Conference on Artificial Neural Networks. vol. 2. 976--981.

[5]

Aeyels, D. 1991. On the dynamic behaviour of the novelty detector and the novelty filter. In Analysis of Controlled Dynamical Systems: Progress in Systems and Control Theory, B. Bonnard, B. Bride, J. Gauthier, and I. Kupka, Eds. vol. 8. Springer, Berlin, 1--10.

[6]

Agarwal, D. 2005. An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays. In Proceedings of the 5th IEEE International Conference on Data Mining. IEEE Computer Society, 26--33.

Digital Library

[7]

Agarwal, D. 2006. Detecting anomalies in cross-classified streams: A Bayesian approach. Knowl. Inform. Syst. 11, 1, 29--44.

Digital Library

[8]

Aggarwal, C. 2005. On abnormality detection in spuriously populated data streams. In Proceedings of the 5th SIAM Data Min. Conference. 80--91.

[9]

Aggarwal, C. and Yu, P. 2001. Outlier detection for high dimensional data. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 37--46.

Digital Library

[10]

Aggarwal, C. C. and Yu, P. S. 2008. Outlier detection with uncertain data. In Proceedings of the International Conference on Data Mining (SDM). 483--493.

[11]

Agovic, A., Banerjee, A., Ganguly, A. R., and Protopopescu, V. 2007. Anomaly detection in transportation corridors using manifold embedding. In Proceedings of the 1st International Workshop on Knowledge Discovery from Sensor Data. ACM Press.

[12]

Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering. IEEE Computer Society, 3--14.

Digital Library

[13]

Agyemang, M., Barker, K., and Alhajj, R. 2006. A comprehensive survey of numeric and symbolic outlier mining techniques. Intel. Data Anal. 10, 6, 521--538.

Digital Library

[14]

Albrecht, S., Busch, J., Kloppenburg, M., Metze, F., and Tavan, P. 2000. Generalized radial basis function networks for classification and novelty detection: Self-organization of optional Bayesian decision. Neural Netw. 13, 10, 1075--1093.

Digital Library

[15]

Aleskerov, E., Freisleben, B., and Rao, B. 1997. Cardwatch: A neural network based database mining system for credit card fraud detection. In Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering. 220--226.

[16]

Allan, J., Carbonell, J., Doddington, G., Yamron, J., and Yang, Y. 1998. Topic detection and tracking pilot study. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. 194--218.

[17]

Anderson, D. Lunt, T. F., Javitz, H., Tamaru, A., and Valdes, A. 1995. Detecting unusual program behavior using the statistical components of NIDES. Tech. rep. SRI--CSL--95--06, Computer Science Laboratory, SRI International.

[18]

Anderson, D., Frivold, T., Tamaru, A., and Valdes, A. 1994. Next-generation intrusion detection expert system (NIDES), software users manual, beta-update release. Tech. rep. SRI--CSL--95--07, Computer Science Laboratory, SRI International.

[19]

Ando, S. 2007. Clustering needles in a haystack: An information theoretic analysis of minority and outlier detection. In Proceedings of the 7th International Conference on Data Mining. 13--22.

Digital Library

[20]

Angiulli, F. and Pizzuti, C. 2002. Fast outlier detection in high dimensional spaces. In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery. Springer-Verlag, 15--26.

Digital Library

[21]

Anscombe, F. J. and Guttman, I. 1960. Rejection of outliers. Technometrics 2, 2, 123--147.

[22]

Arning, A., Agrawal, R., and Raghavan, P. 1996. A linear method for deviation detection in large databases. In Proceedings of the 2nd International Conference of Knowledge Discovery and Data Mining. 164--169.

[23]

Augusteijn, M. and Folkert, B. 2002. Neural network classification and novelty detection. Int. J. Rem. Sens. 23, 14, 2891--2902.

[24]

Bakar, Z., Mohemad, R., Ahmad, A., and Deris, M. 2006. A comparative study for outlier detection techniques in data mining. Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems. 1--6.

[25]

Baker, D., Hofmann, T., McCallum, A., and Yang, Y. 1999. A hierarchical probabilistic model for novelty detection in text. In Proceedings of the International Conference on Machine Learning.

[26]

Barbara, D., Couto, J., Jajodia, S., and Wu, N. 2001a. Adam: A testbed for exploring the use of data mining in intrusion detection. SIGMOD Rec. 30, 4, 15--24.

Digital Library

[27]

Barbara, D., Couto, J., Jajodia, S., and Wu, N. 2001b. Detecting novel network intrusions using Bayes estimators. In Proceedings of the 1st SIAM International Conference on Data Mining.

[28]

Barbara, D., Li, Y., Couto, J., Lin, J.-L., and Jajodia, S. 2003. Bootstrapping a data mining intrusion detection system. In Proceedings of the ACM Symposium on Applied Computing. ACM Press, 421--425.

Digital Library

[29]

Barnett, V. 1976. The ordering of multivariate data (with discussion). J. Royal Statis. Soc. Series A 139, 318--354.

[30]

Barnett, V. and Lewis, T. 1994. Outliers in Statistical Data. John Wiley.

[31]

Barson, P., Davey, N., Field, S. D. H., Frank, R. J., and McAskie, G. 1996. The detection of fraud in mobile phone networks. Neural Netw. World 6, 4.

[32]

Basu, S., Bilenko, M., and Mooney, R. J. 2004. A probabilistic framework for semi-supervised clustering. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 59--68.

Digital Library

[33]

Basu, S. and Meckesheimer, M. 2007. Automatic outlier detection for time series: an application to sensor data. Know. Inform. Syst. 11, 2, 137--154.

Digital Library

[34]

Bay, S. D. and Schwabacher, M. 2003. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 29--38.

Digital Library

[35]

Beckman, R. J. and Cook, R. D. 1983. Outlier...s. Technometrics 25, 2, 119--149.

[36]

Bejerano, G. and Yona, G. 2001. Variations on probabilistic suffix trees: statistical modeling and prediction of protein families. Bioinformatics 17, 1, 23--43.

[37]

Bentley, J. L. 1975. Multi-dimensional binary search trees used for associative searching. Comm. ACM 18, 9, 509--517.

Digital Library

[38]

Bianco, A. M., Ben, M. G., Martinez, E. J., and Yohai, V. J. 2001. Outlier detection in regression models with arima errors using robust estimates. J. Forecast. 20, 8, 565--579.

[39]

Bishop, C. 1994. Novelty detection and neural network validation. In Proceedings of the IEEE Conference on Vision, Image and Signal Processing. vol. 141. 217--222.

[40]

Blender, R., Fraedrich, K., and Lunkeit, F. 1997. Identification of cyclone-track regimes in the north atlantic. Quart. J. Royal Meteor. Soc. 123, 539, 727--741.

[41]

Bolton, R. and Hand, D. 1999. Unsupervised profiling methods for fraud detection. In Proceedings of the Conference on Credit Scoring and Credit Control VII.

[42]

Boriah, S., Chandola, V., and Kumar, V. 2008. Similarity measures for categorical data: A comparative evaluation. In Proceedings of the 8th SIAM International Conference on Data Mining. 243--254.

[43]

Borisyuk, R., Denham, M., Hoppensteadt, F., Kazanovich, Y., and Vinogradova, O. 2000. An oscillatory neural network model of sparse distributed memory and novelty detection. Biosystems 58, 265--272.

[44]

Box, G. E. P. and Tiao, G. C. 1968. Bayesian analysis of some outlier problems. Biometrika 55, 1, 119--129.

[45]

Branch, J., Szymanski, B., Giannella, C., Wolff, R., and Kargupta, H. 2006. In-network outlier detection in wireless sensor networks. In Proceedings of the 26th IEEE International Conference on Distributed Computing Systems.

Digital Library

[46]

Brause, R., Langsdorf, T., and Hepp, M. 1999. Neural data mining for credit card fraud detection. In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence. 103--106.

Digital Library

[47]

Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. 1999. Optics-of: Identifying local outliers. In Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery. Springer-Verlag, 262--270.

Digital Library

[48]

Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. 2000. LOF: Identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 93--104.

Digital Library

[49]

Brito, M. R., Chavez, E. L., Quiroz, A. J., and Yukich, J. E. 1997. Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection. Statis. Prob. Lett. 35, 1, 33--42.

[50]

Brockett, P. L., Xia, X., and Derrig, R. A. 1998. Using Kohonen's self-organizing feature map to uncover automobile bodily injury claims fraud. J. Risk Insur. 65, 2, 245--274.

[51]

Bronstein, A., Das, J., Duro, M., Friedrich, R., Kleyner, G., Mueller, M., Singhal, S., and Cohen, I. 2001. Bayesian networks for detecting anomalies in Internet-based services. In Proceedings of the International Symposium on Integrated Network Management.

[52]

Brotherton, T. and Johnson, T. 2001. Anomaly detection for advanced military aircraft using neural networks. In Proceedings of the IEEE Aerospace Conference.

[53]

Brotherton, T., Johnson, T., and Chadderdon, G. 1998. Classification and novelty detection using linear models and a class dependent-elliptical basis function neural network. In Proceedings of the IJCNN Conference.

[54]

Budalakoti, S., Srivastava, A., Akella, R., and Turkov, E. 2006. Anomaly detection in large sets of high-dimensional symbol sequences. Tech. rep. NASA TM-2006-214553, NASA Ames Research Center.

[55]

Byers, S. D. and Raftery, A. E. 1998. Nearest neighbor clutter removal for estimating features in spatial point processes. J. Amer. Statis. Assoc. 93, 577--584.

[56]

Byungho, H. and Sungzoon, C. 1999. Characteristics of autoassociative MLP as a novelty detector. In Proceedings of the IEEE International Joint Conference on Neural Networks. Vol. 5. 3086--3091.

[57]

Cabrera, J. B. D., Lewis, L., and Mehra, R. K. 2001. Detection and classification of intrusions and faults using sequences of system calls. SIGMOD Rec. 30, 4, 25--34.

Digital Library

[58]

Campbell, C. and Bennett, K. 2001. A linear programming approach to novelty detection. In Proceedings of the Conference on Advances in Neural Information Processing. vol. 14. Cambridge Press.

[59]

Caudell, T. and Newman, D. 1993. An adaptive resonance architecture to define normality and detect novelties in time series and databases. In Proceedings of the IEEE World Congress on Neural Networks. IEEE, 166--176.

[60]

Chakrabarti, S., Sarawagi, S., and Dom, B. 1998. Mining surprising patterns using temporal description length. In Proceedings of the 24rd International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 606--617.

Digital Library

[61]

Chandola, V., Banerjee, A., and Kumar, V. 2007. Anomaly detection: A survey. Tech. rep. 07-017, Computer Science Department, University of Minnesota.

[62]

Chandola, V., Boriah, S., and Kumar, V. 2008. Understanding categorical similarity measures for outlier detection. Tech. rep. 08-008, University of Minnesota.

[63]

Chandola, V., Eilertson, E., Ertoz, L., Simon, G., and Kumar, V. 2006. Data mining for cyber security. In Data Warehousing and Data Mining Techniques for Computer Security, A. Singhal, Ed. Springer.

[64]

Chatzigiannakis, V., Papavassiliou, S., Grammatikou, M., and Maglaris, B. 2006. Hierarchical anomaly detection in distributed large-scale sensor networks. In Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC). IEEE Computer Society, 761--767.

Digital Library

[65]

Chaudhary, A., Szalay, A. S., and Moore, A. W. 2002. Very fast outlier detection in large multidimensional data sets. In Proceedings of the ACM SIGMOD Workshop in Research Issues in Data Mining and Knowledge Discovery (DMKD). ACM Press.

[66]

Chawla, N. V., Japkowicz, N., and Kotcz, A. 2004. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. 6, 1, 1--6.

Digital Library

[67]

Chen, D., Shao, X., Hu, B., and Su, Q. 2005. Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra. Anal. Sci. 21, 2, 161--167.

[68]

Chiu, A. and Chee Fu, A. W. 2003. Enhancements on local outlier detection. In Proceedings of the 7th International Database Engineering and Applications Symposium. 298--307.

[69]

Chow, C. and Yeung, D.-Y. 2002. Parzen-window network intrusion detectors. In Proceedings of the 16th International Conference on Pattern Recognition. vol. 4. IEEE Computer Society, 40385.

Digital Library

[70]

Cox, K. C., Eick, S. G., Wills, G. J., and Brachman, R. J. 1997. Visual data mining: Recognizing telephone calling fraud. J. Data Min. Knowl. Disc. 1, 2, 225--231.

Digital Library

[71]

Crook, P. and Hayes, G. 2001. A robot implementation of a biologically inspired method for novelty detection. In Proceedings of the Towards Intelligent Mobile Robots Conference.

[72]

Crook, P. A., Marsland, S., Hayes, G., and Nehmzow, U. 2002. A tale of two filters: Online novelty detection. In Proceedings of the International Conference on Robotics and Automation. 3894--3899.

[73]

Cun, Y. L., Boser, B., Denker, J. S., Howard, R. E., Hubbard, W., Jackel, L. D., and Henderson, D. 1990. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems. 396--404. Morgan Koufamann.

Digital Library

[74]

Das, K. and Schneider, J. 2007. Detecting anomalous records in categorical datasets. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press.

Digital Library

[75]

Dasgupta, D. and Majumdar, N. 2002. Anomaly detection in multidimensional data using negative selection algorithm. In Proceedings of the IEEE Conference on Evolutionary Computation. 1039--1044.

Digital Library

[76]

Dasgupta, D. and Nino, F. 2000. A comparison of negative and positive selection algorithms in novel pattern detection. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. vol. 1. 125--130.

[77]

Davy, M. and Godsill, S. 2002. Detection of abrupt spectral changes using support vector machines, an application to audio signal segmentation. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing.

[78]

Debar, H., Dacier, M., Nassehi, M., and Wespi, A. 1998. Fixed vs. variable-length patterns for detecting suspicious process behavior. In Proceedings of the 5th European Symposium on Research in Computer Security. Springer-Verlag, 1--15.

Digital Library

[79]

Denning, D. E. 1987. An intrusion detection model. IEEE Trans. Softw. Eng. 13, 2, 222--232.

Digital Library

[80]

Desforges, M., Jacob, P., and Cooper, J. 1998. Applications of probability density estimation to the detection of abnormal conditions in engineering. In Proceedings of the Institute of the Mechanical Engineers. vol. 212. 687--703.

[81]

Diaz, I. and Hollmen, J. 2002. Residual generation and visualization for understanding novel process conditions. In Proceedings of the IEEE International Joint Conference on Neural Networks. IEEE, 2070--2075.

[82]

Diehl, C. and Hampshire, J. 2002. Real-time object classification and novelty detection for collaborative video surveillance. In Proceedings of the IEEE International Joint Conference on Neural Networks. IEEE.

[83]

Donoho, S. 2004. Early detection of insider trading in option markets. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 420--429.

Digital Library

[84]

Dorronsoro, J. R., Ginel, F., Sanchez, C., and Cruz, C. S. 1997. Neural fraud detection in credit card operations. IEEE Trans. Neural Netw. 8, 4, 827--834.

Digital Library

[85]

Du, W., Fang, L., and Peng, N. 2006. Lad: Localization anomaly detection for wireless sensor networks. J. Paral. Distrib. Comput. 66, 7, 874--886.

Digital Library

[86]

Duda, R. O., Hart, P. E., and Stork, D. G. 2000. Pattern Classification 2nd Ed. Wiley-Interscience.

Digital Library

[87]

Dutta, H., Giannella, C., Borne, K., and Kargupta, H. 2007. Distributed top-k outlier detection in astronomy catalogs using the DEMAC system. In Proceedings of the 7th SIAM International Conference on Data Mining.

[88]

Edgeworth, F. Y. 1887. On discordant observations. Philosoph. Mag. 23, 5, 364--375.

[89]

Emamian, V., Kaveh, M., and Tewfik, A. 2000. Robust clustering of acoustic emission signals using the Kohonen network. In Proceedings of the IEEE International Conference of Acoustics, Speech and Signal Processing. IEEE Computer Society.

Digital Library

[90]

Endler, D. 1998. Intrusion detection: Applying machine learning to solaris audit data. In Proceedings of the 14th Annual Computer Security Applications Conference. IEEE Computer Society, 268.

Digital Library

[91]

Ertoz, L., Eilertson, E., Lazarevic, A., Tan, P.-N., Kumar, V., Srivastava, J., and Dokas, P. 2004. MINDS&#8212;Minnesota Intrusion Detection System. In Data Mining&#8212;Next Generation Challenges and Future Directions. MIT Press.

[92]

Ert&#246;z, L., Steinbach, M., and Kumar, V. 2003. Finding topics in collections of documents: A shared nearest neighbor approach. In Clustering and Information Retrieval. 83--104.

[93]

Escalante, H. J. 2005. A comparison of outlier detection algorithms for machine learning. In Proceedings of the International Conference on Communications in Computing.

[94]

Eskin, E. 2000. Anomaly detection over noisy data using learned probability distributions. In Proceedings of the 17th International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., 255--262.

Digital Library

[95]

Eskin, E., Arnold, A., Prerau, M., Portnoy, L., and Stolfo, S. 2002. A geometric framework for unsupervised anomaly detection. In Proceedings of the Conference on Applications of Data Mining in Computer Security. Kluwer Academics, 78--100.

[96]

Eskin, E., Lee, W., and Stolfo, S. 2001. Modeling system call for intrusion detection using dynamic window sizes. In Proceedings of DARPA Information Survivability Conference and Exposition (DISCEX).

[97]

Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, E. Simoudis, J. Han, and U. Fayyad, Eds. AAAI Press, 226--231.

[98]

Fan, W., Miller, M., Stolfo, S. J., Lee, W., and Chan, P. K. 2001. Using artificial anomalies to detect unknown and known network intrusions. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 123--130.

Digital Library

[99]

Fawcett, T. and Provost, F. 1999. Activity monitoring: noticing interesting changes in behavior. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 53--62.

Digital Library

[100]

Forrest, S., D'haeseleer, P., and Helman, P. 1996a. An immunological approach to change detection: Algorithms, analysis and implications. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society, 110.

Digital Library

[101]

Forrest, S., Esponda, F., and Helman, P. 2004. A formal framework for positive and negative detection schemes. In IEEE Trans. Syst. Man Cybernetics, Part B. IEEE, 357--373.

Digital Library

[102]

Forrest, S., Hofmeyr, S. A., Somayaji, A., and Longstaff, T. A. 1996b. A sense of self for unix processes. In Proceedings of the IEEE ISRSP. 120--128.

Digital Library

[103]

Forrest, S., Perelson, A. S., Allen, L., and Cherukuri, R. 1994. Self-nonself discrimination in a computer. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society, 202.

Digital Library

[104]

Forrest, S., Warrender, C., and Pearlmutter, B. 1999. Detecting intrusions using system calls: Alternate data models. In Proceedings of the IEEE ISRSP. IEEE Computer Society, 133--145.

[105]

Fox, A. J. 1972. Outliers in time series. J. Royal Statis. Soc. Series B 34, 3, 350--363.

[106]

Fujimaki, R., Yairi, T., and Machida, K. 2005. An approach to spacecraft anomaly detection problem using kernel feature space. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM Press, 401--410.

Digital Library

[107]

Galeano, P., Pea, D., and Tsay, R. S. 2004. Outlier detection in multivariate time series via projection pursuit. Statistics and econometrics working articles ws044211, Departamento de Estad&#239;stica y Econometr&#239;ca, Universidad Carlos III.

[108]

Ghosh, A. K., Schwartzbard, A., and Schatz, M. 1999a. Learning program behavior profiles for intrusion detection. In Proceedings of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring. 51--62.

Digital Library

[109]

Ghosh, A. K., Schwartzbard, A., and Schatz, M. 1999b. Using program behavior profiles for intrusion detection. In Proceedings of the SANS 3rd Conference and Workshop on Intrusion Detection and Response.

Digital Library

[110]

Ghosh, A. K., Wanken, J., and Charron, F. 1998. Detecting anomalous and unknown intrusions against programs. In Proceedings of the 14th Annual Computer Security Applications Conference. IEEE Computer Society, 259.

Digital Library

[111]

Ghosh, S. and Reilly, D. L. 1994. Credit card fraud detection with a neural-network. In Proceedings of the 27th Annual Hawaii International Conference on System Science. vol. 3.

[112]

Ghoting, A., Parthasarathy, S., and Otey, M. 2006. Fast mining of distance-based outliers in high dimensional datasets. In Proceedings of the SIAM International Conference on Data Mining.

[113]

Gibbons, R. D. 1994. Statistical Methods for Groundwater Monitoring. John Wiley & Sons, Inc.

[114]

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101, 23, e215--e220. Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215.

[115]

Gonzalez, F. A. and Dasgupta, D. 2003. Anomaly detection using real-valued negative selection. Genetic Program. Evolv. Mach. 4, 4, 383--403.

Digital Library

[116]

Grubbs, F. 1969. Procedures for detecting outlying observations in samples. Technometrics 11, 1, 1--21.

[117]

Guha, S., Rastogi, R., and Shim, K. 2000. ROCK: A robust clustering algorithm for categorical attributes. Inform. Syst. 25, 5, 345--366.

Digital Library

[118]

Gunter, S., Schraudolph, N. N., and Vishwanathan, S. V. N. 2007. Fast iterative kernel principal component analysis. J. Mach. Learn. Res. 8, 1893--1918.

Digital Library

[119]

Guttormsson, S. E, Marks R. J. II, El-Sharkawi, M. A., and Kerszenbaum, I. 1999. Elliptical novelty grouping for online short-turn detection of excited running rotors. IEEE Trans. Energy Conv. 14, 1.

[120]

Gwadera, R., Atallah, M. J., and Szpankowski, W. 2004. Detection of significant sets of episodes in event sequences. In Proceedings of the 4th IEEE International Conference on Data Mining. IEEE Computer Society, 3--10.

Digital Library

[121]

Gwadera, R., Atallah, M. J., and Szpankowski, W. 2005a. Markov models for identification of significant episodes. In Proceedings of the 5th SIAM International Conference on Data Mining.

[122]

Gwadera, R., Atallah, M. J., and Szpankowski, W. 2005b. Reliable detection of episodes in event sequences. Knowl. Inform. Syst. 7, 4, 415--437.

Digital Library

[123]

Harris, T. 1993. Neural network in machine health monitoring. Professional Engin.

[124]

Hartigan, J. A. and Wong, M. A. 1979. A k-means clustering algorithm. Appl. Stat. 28, 100--108.

[125]

Hautamaki, V., Karkkainen, I., and Franti, P. 2004. Outlier detection using k-nearest neighbour graph. In Proceedings of the 17th International Conference on Pattern Recognition. vol. 3. IEEE Computer Society, 430--433.

Digital Library

[126]

Hawkins, D. 1980. Identification of Outliers. Chapman and Hall, London and New York.

[127]

Hawkins, D. M. 1974. The detection of errors in multivariate data using principal components. J. Amer. Statis. Assoc. 69, 346, 340--344.

[128]

Hawkins, S., He, H., Williams, G. J., and Baxter, R. A. 2002. Outlier detection using replicator neural networks. In Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery. Springer-Verlag, 170--180.

Digital Library

[129]

Hazel, G. G. 2000. Multivariate Gaussian MRF for multi-spectral scene segmentation and anomaly detection. GeoRS 38, 3, 1199--1211.

[130]

He, H., Wang, J., Graco, W., and Hawkins, S. 1997. Application of neural networks to detection of medical fraud. Expert Syst. Appl. 13, 4, 329--336.

[131]

He, Z., Deng, S., and Xu, X. 2002. Outlier detection integrating semantic knowledge. In Proceedings of the 3rd International Conference on Advances in Web-Age Information Management. Springer-Verlag, 126--131.

Digital Library

[132]

He, Z., Deng, S., Xu, X., and Huang, J. Z. 2006. A fast greedy algorithm for outlier mining. In Proceedings of the 10th Pacific-Asia Conference on Knowledge and Data Discovery. 567--576.

Digital Library

[133]

He, Z., Xu, X., and Deng, S. 2003. Discovering cluster-based local outliers. Pattern Recog. Lett. 24, 9--10, 1641--1650.

Digital Library

[134]

He, Z., Xu, X., and Deng, S. 2005. An optimization model for outlier detection in categorical data. In Proceedings of the International Conference on Intelligent Computing. Lecture Notes in Computer Science, vol. 3644. Springer.

Digital Library

[135]

He, Z., Xu, X., Huang, J. Z., and Deng, S. 2004a. A Frequent Pattern Discovery Method for Outlier Detection. Springer, 726--732.

[136]

He, Z., Xu, X., Huang, J. Z., and Deng, S. 2004b. Mining Class Outliers: Concepts, Algorithms and Applications. Springer, 588--589.

[137]

Heller, K. A., Svore, K. M., Keromytis, A. D., and Stolfo, S. J. 2003. One class support vector machines for detecting anomalous windows registry accesses. In Proceedings of the Workshop on Data Mining for Computer Security.

[138]

Helman, P. and Bhangoo, J. 1997. A statistically-based system for prioritizing information exploration under uncertainty. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. vol. 27. IEEE, 449--466.

[139]

Helmer, G., Wong, J., Honavar, V., and Miller, L. 1998. Intelligent agents for intrusion detection. In Proceedings of the IEEE Information Technology Conference. 121--124.

[140]

Hickinbotham, S. J. and Austin, J. 2000a. Novelty detection in airframe strain data. In Proceedings of the 15th International Conference on Pattern Recognition. Vol. 2. 536--539.

[141]

Hickinbotham, S. J. and Austin, J. 2000b. Novelty detection in airframe strain data. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. vol. 6. 24--27.

[142]

Ho, L. L., Macey, C. J., and Hiller, R. 1999. A distributed and reliable platform for adaptive anomaly detection in IP networks. In Proceedings of the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management. Springer-Verlag, 33--46.

Digital Library

[143]

Ho, T. V. and Rouat, J. 1997. A novelty detector using a network of integrate and fire neurons. Lecture Notes in Computer Science, vol. 1327. 103--108.

Digital Library

[144]

Ho, T. V. and Rouat, J. 1998. Novelty detection based on relaxation time of a network of integrate-and-fire neurons. In Proceedings of the 2nd IEEE World Congress on Computational Intelligence. 1524--1529.

[145]

Hodge, V. and Austin, J. 2004. A survey of outlier detection methodologies. Artif. Intel. Rev. 22, 2, 85--126.

Digital Library

[146]

Hofmeyr, S. A., Forrest, S., and Somayaji, A. 1998. Intrusion detection using sequences of system calls. J. Comput. Secur. 6, 3, 151--180.

Digital Library

[147]

Hollier, G. and Austin, J. 2002. Novelty detection for strain-gauge degradation using maximally correlated components. In Proceedings of the European Symposium on Artificial Neural Networks. 257--262--539.

[148]

Hollmen, J. and Tresp, V. 1999. Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In Proceedings of the Conference on Advances in Neural Information Processing Systems II. MIT Press, 889--895.

Digital Library

[149]

Horn, P. S., Feng, L., Li, Y., and Pesce, A. J. 2001. Effect of outliers and nonhealthy individuals on reference interval estimation. Clinical Chem. 47, 12, 2137--2145.

[150]

Hu, W., Liao, Y., and Vemuri, V. R. 2003. Robust anomaly detection using support vector machines. In Proceedings of the International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., 282--289.

[151]

Huber, P. 1974. Robust Statistics. Wiley, New York.

[152]

Huber, P. J. 1985. Projection pursuit (with discussions). Ann. Stat. 13, 2, 435--475.

[153]

Ide, T. and Kashima, H. 2004. Eigenspace-based anomaly detection in computer systems. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 440--449.

Digital Library

[154]

Id&#233;, T., Papadimitriou, S., and Vlachos, M. 2007. Computing correlation anomaly scores using stochastic nearest neighbors. In Proceedings of the International Conference Data Mining. 523--528.

Digital Library

[155]

Ihler, A., Hutchins, J., and Smyth, P. 2006. Adaptive event detection with time-varying Poisson processes. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 207--216.

Digital Library

[156]

Ilgun, K., Kemmerer, R. A., and Porras, P. A. 1995. State transition analysis: A rule-based intrusion detection approach. IEEE Trans. Softw. Eng. 21, 3, 181--199.

Digital Library

[157]

Jagadish, H. V., Koudas, N., and Muthukrishnan, S. 1999. Mining deviants in a time series database. In Proceedings of the 25th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 102--113.

Digital Library

[158]

Jagota, A. 1991. Novelty detection on a very large number of memories stored in a hopfield-style network. In Proceedings of the International Joint Conference on Neural Networks. vol. 2. 905.

[159]

Jain, A. K. and Dubes, R. C. 1988. Algorithms for Clustering Data. Prentice-Hall, Inc.

Digital Library

[160]

Jakubek, S. and Strasser, T. 2002. Fault-diagnosis using neural networks with ellipsoidal basis functions. In Proceedings of the American Control Conference. vol. 5. 3846--3851.

[161]

Janakiram, D., Reddy, V., and Kumar, A. 2006. Outlier detection in wireless sensor networks using Bayesian belief networks. In Proceedings of the 1st International Conference on Communication System Software and Middleware. 1--6.

[162]

Japkowicz, N., Myers, C., and Gluck, M. A. 1995. A novelty detection approach to classification. In Proceedings of the International Joint Conference on Artificial Intelligence. 518--523.

Digital Library

[163]

Javitz, H. S. and Valdes, A. 1991. The SRI IDES statistical anomaly detector. In Proceedings of the IEEE Symposium on Research in Security and Privacy. IEEE Computer Society.

[164]

Jiang, M. F., Tseng, S. S., and Su, C. M. 2001. Two-phase clustering process for outliers detection. Patt. Recog. Lett. 22, 6-7, 691--700.

Digital Library

[165]

Jin, W., Tung, A. K. H., and Han, J. 2001. Mining top-n local outliers in large databases. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 293--298.

Digital Library

[166]

Joachims, T. 2006. Training linear SVMS in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 217--226.

Digital Library

[167]

Jolliffe, I. T. 2002. Principal Component Analysis, 2nd Ed. Springer.

[168]

Joshi, M. V., Agarwal, R. C., and Kumar, V. 2001. Mining needle in a haystack: classifying rare classes via two-phase rule induction. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 91--102.

Digital Library

[169]

Joshi, M. V., Agarwal, R. C., and Kumar, V. 2002. Predicting rare classes: can boosting make any weak learner strong&quest; In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 297--306.

Digital Library

[170]

Kadota, K., Tominaga, D., Akiyama, Y., and Takahashi, K. 2003. Detecting outlying samples in micro-array data: A critical assessment of the effect of outliers on sample classification. Chem-Bio Informatics 3, 1, 30--45.

[171]

Karypis, G. and Kumar, V. 1998. Multi-level k-way partitioning scheme for irregular graphs. J. Paral. Distrib. Comput. 48, 1, 96--129.

Digital Library

[172]

Kearns, M. J. 1990. Computational Complexity of Machine Learning. MIT Press.

Digital Library

[173]

Keogh, E., Lin, J., Lee, S.-H., and Herle, H. V. 2006. Finding the most unusual time series subsequence: Algorithms and applications. Knowl. Inform. Syst. 11, 1, 1--27.

Digital Library

[174]

Keogh, E., Lonardi, S., and chi' Chiu, B. Y. 2002. Finding surprising patterns in a time series database in linear time and space. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 550--556.

Digital Library

[175]

Keogh, E., Lonardi, S., and Ratanamahatana, C. A. 2004. Towards parameter-free data mining. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 206--215.

Digital Library

[176]

Keogh, E. and Smyth, P. 1997. A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, D. Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, Eds. AAAI Press, 24--30.

[177]

King, S., King, D., P. Anuzis, K. A., Tarassenko, L., Hayton, P., and Utete, S. 2002. The use of novelty detection techniques for monitoring high-integrity plant. In Proceedings of the International Conference on Control Applications. vol. 1., 221--226.

[178]

Kitagawa, G. 1979. On the use of AIC for the detection of outliers. Technometrics 21, 2, 193--199.

[179]

Knorr, E. M. and Ng, R. T. 1997. A unified approach for mining outliers. In Proceedings of the Conference of the Centre for Advanced Studies on Collaborative Research. IBM Press, 11.

Digital Library

[180]

Knorr, E. M. and Ng, R. T. 1998. Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24rd International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 392--403.

Digital Library

[181]

Knorr, E. M. and Ng, R. T. 1999. Finding intensional knowledge of distance-based outliers. VLDB J. 211--222.

Digital Library

[182]

Knorr, E. M., Ng, R. T., and Tucakov, V. 2000. Distance-based outliers: Algorithms and applications. VLDB J. 8, 3-4, 237--253.

Digital Library

[183]

Ko, H. and Jacyna, G. 2000. Dynamical behavior of autoassociative memory performing novelty filtering. In IEEE Trans. Neural Netw. Vol. 11. 1152--1161.

Digital Library

[184]

Kohonen, T., Ed. 1997. Self-Organizing Maps. Springer-Verlag.

Digital Library

[185]

Kojima, K. and Ito, K. 1999. Autonomous learning of novel patterns by utilizing chaotic dynamics. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. Vol. 1. IEEE, 284--289.

[186]

Kosoresow, A. P. and Hofmeyr, S. A. 1997. Intrusion detection via system call traces. IEEE Softw. 14, 5, 35--42.

Digital Library

[187]

Kou, Y., Lu, C.-T., and Chen, D. 2006. Spatial weighted outlier detection. In Proceedings of the SIAM Conference on Data Mining.

[188]

Kruegel, C., Mutz, D., Robertson, W., and Valeur, F. 2003. Bayesian event classification for intrusion detection. In Proceedings of the 19th Annual Computer Security Applications Conference. IEEE Computer Society, 14.

Digital Library

[189]

Kruegel, C., Toth, T., and Kirda, E. 2002. Service specific anomaly detection for network intrusion detection. In Proceedings of the ACM symposium on Applied Computing. ACM Press, 201--208.

Digital Library

[190]

Kruegel, C. and Vigna, G. 2003. Anomaly detection of Web-based attacks. In Proceedings of the 10th ACM Conference on Computer and Communications Security. ACM Press, 251--261.

Digital Library

[191]

Kumar, V. 2005. Parallel and distributed computing for cybersecurity. IEEE Distrib. Syst. Online 6, 10.

Digital Library

[192]

Labib, K. and Vemuri, R. 2002. NSOM: A real-time network-based intrusion detection using self-organizing maps. Netw. Security.

[193]

Lakhina, A., Crovella, M., and Diot, C. 2005. Mining anomalies using traffic feature distributions. In Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. ACM Press, 217--228.

Digital Library

[194]

Lane, T. and Brodley, C. E. 1997a. An application of machine learning to anomaly detection. In Proceedings of the Conference on 20th NIST-NCSC National Information Systems Security Conference. 366--380.

[195]

Lane, T. and Brodley, C. E. 1997b. Sequence matching and learning in anomaly detection for computer security. In Proceedings of the Conference on AI Approaches to Fraud Detection and Risk Management, Fawcett, Haimowitz, Provost, and Stolfo, Eds. AAAI Press, 43--49.

[196]

Lane, T. and Brodley, C. E. 1999. Temporal sequence learning and data reduction for anomaly detection. ACM Trans. Inform. Syst. Secur. 2, 3, 295--331.

Digital Library

[197]

Lauer, M. 2001. A mixture approach to novelty detection using training data with outliers. In Proceedings of the 12th European Conference on Machine Learning. Springer-Verlag, 300--311.

Digital Library

[198]

Laurikkala, J., Juhola, M., and Kentala., E. 2000. Informal identification of outliers in medical data. In Proceedings of the 5<sup>th</sup> International Workshop on Intelligent Data Analysis in Medicine and Pharmacology. 20--24.

[199]

Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., and Srivastava, J. 2003. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the SIAM International Conference on Data Mining. (SIAM).

[200]

Lee, W. and Stolfo, S. 1998. Data mining approaches for intrusion detection. In Proceedings of the 7th USENIX Security Symposium.

Digital Library

[201]

Lee, W., Stolfo, S., and Chan, P. 1997. Learning patterns from UNIX process execution traces for intrusion detection. In Proceedings of the AAAI Workshop on AI Methods in Fraud and Risk Management.

[202]

Lee, W., Stolfo, S. J., and Mok, K. W. 2000. Adaptive intrusion detection: A data mining approach. Artif. Intell. Rev. 14, 6, 533--567.

Digital Library

[203]

Lee, W. and Xiang, D. 2001. Information-theoretic measures for anomaly detection. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society, 130.

Digital Library

[204]

Li, M. and Vitanyi, P. M. B. 1993. An Introduction to Kolmogorov Complexity and Its Applications. Springer-Verlag.

Digital Library

[205]

Li, Y., Pont, M. J., and Jones, N. B. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where unknown faults may occur. Patt. Recog. Lett. 23, 5, 569--577.

Digital Library

[206]

Lin, J., Keogh, E., Fu, A., and Herle, H. V. 2005. Approximations to magic: Finding unusual medical time series. In Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems. IEEE Computer Society, 329--334.

Digital Library

[207]

Lin, S. and Brown, D. E. 2003. An outlier-based data association method for linking criminal incidents. In Proceedings of the 3rd SIAM Data Mining Conference.

[208]

Liu, J. P. and Weng, C. S. 1991. Detection of outlying data in bioavailability/bioequivalence studies. Stat. Med. 10, 9, 1375--89.

[209]

Lu, C.-T., Chen, D., and Kou, Y. 2003. Algorithms for spatial outlier detection. In Proceedings of the 3rd International Conference on Data Mining. 597--600.

Digital Library

[210]

Ma, J. and Perkins, S. 2003a. Online novelty detection on temporal sequences. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 613--618.

Digital Library

[211]

Ma, J. and Perkins, S. 2003b. Time-series novelty detection using one-class support vector machines. In Proceedings of the International Joint Conference on Neural Networks. Vol. 3. 1741--1745.

[212]

MacDonald, J. W. and Ghosh, D. 2007. Copa--cancer outlier profile analysis. Bioinformatics 22, 23, 2950--2951.

Digital Library

[213]

Mahoney, M. V. and Chan, P. K. 2002. Learning nonstationary models of normal network traffic for detecting novel attacks. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 376--385.

Digital Library

[214]

Mahoney, M. V. and Chan, P. K. 2003. Learning rules for anomaly detection of hostile network traffic. In Proceedings of the 3rd IEEE International Conference on Data Mining. IEEE Computer Society, 601.

Digital Library

[215]

Mahoney, M. V., Chan, P. K., and Arshad, M. H. 2003. A machine learning approach to anomaly detection. Tech. rep. CS--2003--06, Department of Computer Science, Florida Institute of Technology Melbourne.

[216]

Manevitz, L. M. and Yousef, M. 2000. Learning from positive data for document classification using neural networks. In Proceedings of the 2nd Bar-Ilan Workshop on Knowledge Discovery and Learning.

[217]

Manevitz, L. M. and Yousef, M. 2002. One-class SVMS for document classification. J. Mach. Learn. Res. 2, 139--154.

Digital Library

[218]

Manikopoulos, C. and Papavassiliou, S. 2002. Network intrusion and fault detection: A statistical anomaly approach. IEEE Comm. Mag. 40.

Digital Library

[219]

Manson, G. 2002. Identifying damage sensitive, environment insensitive features for damage detection. In Proceedings of IES Conference.

[220]

Manson, G., Pierce, G., and Worden, K. 2001. On the long-term stability of normal conditions for damage detection in a composite panel. In Proceedings of the 4th International Conference on Damage Assessment of Structures. Cardiff, UK.

[221]

Manson, G., Pierce, S. G., Worden, K., Monnier, T., Guy, P., and Atherton, K. 2000. Long-term stability of normal condition data for novelty detection. In Proceedings of the Conference on Smart Structures and Integrated Systems. 323--334.

[222]

Marceau, C. 2000. Characterizing the behavior of a program using multiple-length n-grams. In Proceedings of the Workshop on New Security Paradigms. ACM Press, 101--110.

Digital Library

[223]

Marchette, D. 1999. A statistical method for profiling network traffic. In Proceedings of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring. 119--128.

Digital Library

[224]

Markou, M. and Singh, S. 2003a. Novelty detection: A review-part 1: Statistical approaches. Sig. Proc. 83, 12, 2481--2497.

Digital Library

[225]

Markou, M. and Singh, S. 2003b. Novelty detection: A review-part 2: Neural network based approaches. Sig. Proc. 83, 12, 2499--2521.

Digital Library

[226]

Marsland, S., Nehmzow, U., and Shapiro, J. 1999. A model of habituation applied to mobile robots. In Proceedings of Towards Intelligent Mobile Robots Conference. Department of Computer Science, Manchester University, Technical rep. UMCS-99-3-1.

[227]

Marsland, S., Nehmzow, U., and Shapiro, J. 2000a. Novelty detection for robot neotaxis. In Proceedings of the 2nd International Symposium on Neural Compuatation. 554--559.

[228]

Marsland, S., Nehmzow, U., and Shapiro, J. 2000b. A real-time novelty detector for a mobile robot. In Proceedings of the EUREL Conference on Advanced Robotics Systems.

[229]

Martinelli, G. and Perfetti, R. 1994. Generalized cellular neural network for novelty detection. IEEE Trans. Circ. Syst. I: Fundamental Theory Application 41, 2, 187--190.

[230]

Martinez, D. 1998. Neural tree density estimation for novelty detection. IEEE Trans. Neural Netw. 9, 2, 330--338.

Digital Library

[231]

McCallum, A., Nigam, K., and Ungar, L. H. 2000. Efficient clustering of high-dimensional data sets with application to reference matching. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 169--178.

Digital Library

[232]

McNeil, A. 1999. Extreme value theory for risk managers. In Internal Modelling and CAD II, 93--113.

[233]

Mingming, N. Y. 2000. Probabilistic networks with undirected links for anomaly detection. In Proceedings of the IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop. 175--179.

[234]

Motulsky, H. 1995. Intuitive Biostatistics: Choosing a Statistical Test. Oxford University Press, Chapter 37.

[235]

Moya, M., Koch, M., and Hostetler, L. 1993. One-class classifier networks for target recognition applications. In Proceedings of the World Congress on Neural Networks, International Neural Network Society. 797--801.

[236]

Murray, A. F. 2001. Novelty detection using products of simple experts: A potential architecture for embedded systems. Neural Netw. 14, 9, 1257--1264.

Digital Library

[237]

Nairac, A., Corbett-Clark, T., Ripley, R., Townsend, N., and Tarassenko, L. 1997. Choosing an appropriate model for novelty detection. In Proceedings of the 5th IEEE International Conference on Artificial Neural Networks. 227--232.

[238]

Nairac, A., Townsend, N., Carr, R., King, S., Cowley, P., and Tarassenko, L. 1999. A system for the analysis of jet engine vibration data. Integ. Comput.-Aided Eng. 6, 1, 53--56.

Digital Library

[239]

Ng, R. T. and Han, J. 1994. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 144--155.

Digital Library

[240]

Noble, C. C. and Cook, D. J. 2003. Graph-based anomaly detection. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 631--636.

Digital Library

[241]

Odin, T. and Addison, D. 2000. Novelty detection using neural network technology. In Proceedings of the COMADEN Conference.

[242]

Otey, M., Parthasarathy, S., Ghoting, A., Li, G., Narravula, S., and Panda, D. 2003. Towards NIC-based intrusion detection. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 723--728.

Digital Library

[243]

Otey, M. E., Ghoting, A., and Parthasarathy, S. 2006. Fast distributed outlier detection in mixed-attribute data sets. Data Min. Knowl. Disc. 12, 2-3, 203--228.

Digital Library

[244]

Palshikar, G. K. 2005. Distance-based outliers in sequences. Lecture Notes in Computer Science, vol. 3816, 547--552.

Digital Library

[245]

Papadimitriou, S., Kitagawa, H., Gibbons, P. B., and Faloutsos, C. 2002. Loci: Fast outlier detection using the local correlation integral. Tech. rep. IRP-TR-02-09, Intel Research Laboratory.

[246]

Parra, L., Deco, G., and Miesbach, S. 1996. Statistical independence and novelty detection with information preserving nonlinear maps. Neural Comput. 8, 2, 260--269.

Digital Library

[247]

Parzen, E. 1962. On the estimation of a probability density function and mode. Annals Math. Stat. 33, 1065--1076.

[248]

Patcha, A. and Park, J.-M. 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw. 51, 12, 3448--3470.

Digital Library

[249]

Petsche, T., Marcantonio, A., Darken, C., Hanson, S., Kuhn, G., and Santoso, I. 1996. A neural network autoassociator for induction motor failure prediction. In Proceedings of the Conference on Advances in Neural Information Processing. vol. 8. 924--930.

[250]

Phoha, V. V. 2002. The Springer Internet Security Dictionary. Springer-Verlag.

Digital Library

[251]

Phua, C., Alahakoon, D., and Lee, V. 2004. Minority report in fraud detection: Classification of skewed data. SIGKDD Explorer Newsletter 6, 1, 50--59.

Digital Library

[252]

Phuong, T. V., Hung, L. X., Cho, S. J., Lee, Y., and Lee, S. 2006. An anomaly detection algorithm for detecting attacks in wireless sensor networks. Intel. Secur. Inform. 3975, 735--736.

Digital Library

[253]

Pickands, J. 1975. Statistical inference using extreme order statistics. Annals Stat. 3, 1, 119--131.

[254]

Pires, A. and Santos-Pereira, C. 2005. Using clustering and robust estimators to detect outliers in multivariate data. In Proceedings of the International Conference on Robust Statistics.

[255]

Platt, J. 2000. Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods. In Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schoelkopf, and D. Schuurmans, Eds. MIT Press, 61--74.

[256]

Pokrajac, D., Lazarevic, A., and Latecki, L. J. 2007. Incremental local outlier detection for data streams. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining.

[257]

Porras, P. A. and Neumann, P. G. 1997. EMERALD: Event monitoring enabling responses to anomalous live disturbances. In Proceedings of the 20th NIST-NCSC National Information Systems Security Conference. 353--365.

[258]

Portnoy, L., Eskin, E., and Stolfo, S. 2001. Intrusion detection with unlabeled data using clustering. In Proceedings of the ACM Workshop on Data Mining Applied to Security.

[259]

Protopapas, P., Giammarco, J. M., Faccioli, L., Struble, M. F., Dave, R., and Alcock, C. 2006. Finding outlier light curves in catalogues of periodic variable stars. Monthly Notices Royal Astronomical Soc. 369, 2, 677--696.

[260]

Qin, M. and Hwang, K. 2004. Frequent episode rules for Internet anomaly detection. In Proceedings of the 3rd IEEE International Symposium on Network Computing and Applications. IEEE Computer Society.

Digital Library

[261]

Ramadas, M., Ostermann, S., and Tjaden, B. C. 2003. Detecting anomalous network traffic with self-organizing maps. In Proceedings of the Conference on Recent Advances in Intrusion Detection. 36--54.

[262]

Ramaswamy, S., Rastogi, R., and Shim, K. 2000. Efficient algorithms for mining outliers from large data sets. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 427--438.

Digital Library

[263]

Ratsch, G., Mika, S., Scholkopf, B., and Muller, K.-R. 2002. Constructing boosting algorithms from SVMS: An application to one-class classification. IEEE Trans. Patt. Anal. Mach. Intel. 24, 9, 1184--1199.

Digital Library

[264]

Roberts, S. 1999. Novelty detection using extreme value statistics. In Proceedings of the IEEE Vision, Image and Signal Processing Conference Vol. 146. 124--129.

[265]

Roberts, S. 2002. Extreme value statistics for novelty detection in biomedical signal processing. In Proceedings of the 1st International Conference on Advances in Medical Signal and Information Processing. 166--172.

[266]

Roberts, S. and Tarassenko, L. 1994. A probabilistic resource allocating network for novelty detection. Neural Comput. 6, 2, 270--284.

Digital Library

[267]

Rosner, B. 1983. Percentage points for a generalized ESD many-outlier procedure. Technometrics 25, 2, 165--172.

[268]

Roth, V. 2004. Outlier detection with one-class kernel Fisher discriminants. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).

[269]

Roth, V. 2006. Kernel fisher discriminants for outlier detection. Neural Comput. 18, 4, 942--960.

Digital Library

[270]

Rousseeuw, P. J. and Leroy, A. M. 1987. Robust Regression and Outlier Detection. John Wiley & Sons, Inc.

Digital Library

[271]

Roussopoulos, N., Kelley, S., and Vincent, F. 1995. Nearest neighbor queries. In Proceedings of the ACM-SIGMOD International Conference on Management of Data.

Digital Library

[272]

Ruotolo, R. and Surace, C. 1997. A statistical approach to damage detection through vibration monitoring. In Proceedings of the 5th Pan-American Congress of Applied Mechanics.

[273]

Salvador, S. and Chan, P. 2003. Learning states and rules for time-series anomaly detection. Tech. rep. CS--2003--05, Department of Computer Science, Florida Institute of Technology Melbourne.

[274]

Sarawagi, S., Agrawal, R., and Megiddo, N. 1998. Discovery-driven exploration of OLAP data cubes. In Proceedings of the 6th International Conference on Extending Database Technology. Springer-Verlag, 168--182.

Digital Library

[275]

Sargor, C. 1998. Statistical anomaly detection for link-state routing protocols. In Proceedings of the 6th International Conference on Network Protocols. IEEE Computer Society, 62.

Digital Library

[276]

Saunders, R. and Gero, J. 2000. The importance of being emergent. In Proceedings of the Conference on Artificial Intelligence in Design.

[277]

Scarth, G., McIntyre, M., Wowk, B., and Somorjai, R. 1995. Detection of novelty in functional images using fuzzy clustering. In Proceedings of the 3rd Meeting of the International Society for Magnetic Resonance in Medicine. 238.

[278]

Sch&#246;lkopf, B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., and Williamson, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 7, 1443--1471.

Digital Library

[279]

Scott, S. L. 2001. Detecting network intrusion using a Markov modulated nonhomogeneous Poisson Process. Journal of the American Statistical Association.

[280]

Sebyala, A. A., Olukemi, T., and Sacks, L. 2002. Active platform security through intrusion detection using naive Bayesian network for anomaly detection. In Proceedings of the London Communications Symposium.

[281]

Sequeira, K. and Zaki, M. 2002. Admit: Anomaly-based data mining for intrusions. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 386--395.

Digital Library

[282]

Sheikholeslami, G., Chatterjee, S., and Zhang, A. 1998. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24rd International Conference on Very Large Databases. Morgan Kaufmann Publishers Inc., 428--439.

Digital Library

[283]

Shekhar, S., Lu, C.-T., and Zhang, P. 2001. Detecting graph-based spatial outliers: Algorithms and applications (a summary of results). In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 371--376.

Digital Library

[284]

Shewhart, W. A. 1931. Economic Control of Quality of Manufactured Product. D. Van Nostrand Company.

[285]

Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., and Chang, L. 2003. A novel anomaly detection scheme-based on principal component classifier. In Proceedings of the 3rd IEEE International Conference on Data Mining. 353--365.

[286]

Siaterlis, C. and Maglaris, B. 2004. Towards multi-sensor data fusion for dos detection. In Proceedings of the ACM Symposium on Applied Computing. ACM Press, 439--446.

Digital Library

[287]

Singh, S. and Markou, M. 2004. An approach to novelty detection applied to the classification of image regions. IEEE Trans. Knowl. Data Eng. 16, 4, 396--407.

Digital Library

[288]

Smith, R., Bivens, A., Embrechts, M., Palagiri, C., and Szymanski, B. 2002. Clustering approaches for anomaly-based intrusion detection. In Proceedings of the Intelligent Engineering Systems through Artificial Neural Networks. ASME Press, 579--584.

[289]

Smyth, P. 1994. Markov monitoring with unknown states. IEEE J. Select. Areas Comm. (Special Issue on Intelligent Signal Processing for Communications) 12, 9, 1600--1612.

Digital Library

[290]

Snyder, D. 2001. Online intrusion detection using sequences of system calls. M.S. thesis, Department of Computer Science, Florida State University.

[291]

Sohn, H., Worden, K., and Farrar, C. 2001. Novelty detection under changing environmental conditions. In Proceedings of the 8th Annual SPIE International Symposium on Smart Structures and Materials.

[292]

Solberg, H. E. and Lahti, A. 2005. Detection of outliers in reference distributions: Performance of Horn's algorithm. Clinical Chem. 51, 12, 2326--2332.

[293]

Song, Q., Hu, W., and Xie, W. 2002. Robust support vector machine with bullet hole image classification. IEEE Trans. Syst. Man Cyber.&#8212;Part C: Applications and Reviews 32, 4.

Digital Library

[294]

Song, S., Shin, D., and Yoon, E. 2001. Analysis of novelty detection properties of auto-associators. In Proceedings of the Conference on Condition Monitoring and Diagnostic Engineering Management. 577--584.

[295]

Song, X., Wu, M., Jermaine, C., and Ranka, S. 2007. Conditional anomaly detection. IEEE Trans. Knowl. Data Eng. 19, 5, 631--645.

Digital Library

[296]

Soule, A., Salamatian, K., and Taft, N. 2005. Combining filtering and statistical methods for anomaly detection. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement. ACM, 1--14.

Digital Library

[297]

Spence, C., Parra, L., and Sajda, P. 2001. Detection, synthesis and compression in mammographic image analysis with a hierarchical image probability model. In Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. IEEE Computer Society, 3.

Digital Library

[298]

Srivastava, A. 2006. Enabling the discovery of recurring anomalies in aerospace problem reports using high-dimensional clustering techniques. In Proceedings of the IEEE Aerospace Conference, 17--34.

[299]

Srivastava, A. and Zane-Ulman, B. 2005. Discovering recurring anomalies in text reports regarding complex space systems. In Proceedings of the IEEE Aerospace Conference, 3853--3862.

[300]

Stefano, C., Sansone, C., and Vento, M. 2000. To reject or not to reject: that is the question: An answer in the case of neural classifiers. IEEE Trans. Syst. Manag. Cyber. 30, 1, 84--94.

Digital Library

[301]

Stefansky, W. 1972. Rejecting outliers in factorial designs. Technometrics 14, 2, 469--479.

[302]

Steinwart, I., Hush, D., and Scovel, C. 2005. A classification framework for anomaly detection. J. Mach. Learn. Res. 6, 211--232.

Digital Library

[303]

Streifel, R., Maks, R., and El-Sharkawi, M. 1996. Detection of shorted-turns in the field of turbine-generator rotors using novelty detectors--development and field tests. IEEE Trans. Energy Conv. 11, 2, 312--317.

[304]

Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., and Gunopulos, D. 2006. Online outlier detection in sensor data using non-parametric models. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB). VLDB Endowment, 187--198.

Digital Library

[305]

Sun, H., Bao, Y., Zhao, F., Yu, G., and Wang, D. 2004. CD-trees: An efficient index structure for outlier detection. In Proceedings of the 5th International Conference on Web-Age Information Management (WAIM). 600--609.

[306]

Sun, J., Qu, H., Chakrabarti, D., and Faloutsos, C. 2005. Neighborhood formation and anomaly detection in bipartite graphs. In Proceedings of the 5th IEEE International Conference on Data Mining. IEEE Computer Society, 418--425.

Digital Library

[307]

Sun, J., Xie, Y., Zhang, H., and Faloutsos, C. 2007. Less is more: Compact matrix representation of large sparse graphs. In Proceedings of the 7th SIAM International Conference on Data Mining.

[308]

Sun, P. and Chawla, S. 2004. On local spatial outliers. In Proceedings of the 4th IEEE International Conference on Data Mining. 209--216.

Digital Library

[309]

Sun, P. and Chawla, S. 2006. SLOM: A new measure for local spatial outliers. Knowl. Inform. Syst. 9, 4, 412--429.

[310]

Sun, P., Chawla, S., and Arunasalam, B. 2006. Mining for outliers in sequential databases. In Proceedings of the SIAM International Conference on Data Mining.

[311]

Surace, C. and Worden, K. 1998. A novelty detection method to diagnose damage in structures: An application to an offshore platform. In Proceedings of the 8th International Conference of Off-Shore and Polar Engineering. vol. 4. Colorado, 64--70.

[312]

Surace, C., Worden, K., and Tomlinson, G. 1997. A novelty detection approach to diagnose damage in a cracked beam. In Proceedings of the SPIE. vol. 3089. 947--953.

[313]

Suzuki, E., Watanabe, T., Yokoi, H., and Takabayashi, K. 2003. Detecting interesting exceptions from medical test data with visual summarization. In Proceedings of the 3rd IEEE International Conference on Data Mining. 315--322.

Digital Library

[314]

Sykacek, P. 1997. Equivalent error bars for neural network classifiers trained by Bayesian inference. In Proceedings of the European Symposium on Artificial Neural Networks. 121--126.

[315]

Tan, P.-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining. Addison-Wesley.

Digital Library

[316]

Tandon, G. and Chan, P. 2007. Weighting versus pruning in rule validation for detecting network and host anomalies. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press.

Digital Library

[317]

Tang, J., Chen, Z., chee Fu, A. W., and W. Cheung, D. 2002. Enhancing effectiveness of outlier detections for low density patterns. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 535--548.

Digital Library

[318]

Taniguchi, M., Haft, M., Hollmn, J., and Tresp, V. 1998. Fraud detection in communications networks using neural and probabilistic methods. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. vol. 2. IEEE Computer Society, 1241--1244.

[319]

Tao, Y., Xiao, X., and Zhou, S. 2006. Mining distance-based outliers from large databases in any metric space. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 394--403.

Digital Library

[320]

Tarassenko, L. 1995. Novelty detection for the identification of masses in mammograms. In Proceedings of the 4th IEEE International Conference on Artificial Neural Networks. vol. 4. 442--447.

[321]

Tax, D. and Duin, R. 1999a. Data domain description using support vectors. In Proceedings of the European Symposium on Artificial Neural Networks, M. Verleysen, Ed., 251--256.

[322]

Tax, D. and Duin, R. 1999b. Support vector data description. Patt. Recog. Lett. 20, 11-13, 1191--1199.

Digital Library

[323]

Tax, D. M. J. 2001. One-class classification; concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology.

[324]

Teng, H., Chen, K., and Lu, S. 1990. Adaptive real-time anomaly detection using inductively generated sequential patterns. In Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy. IEEE Computer Society Press, 278--284.

[325]

Theiler, J. and Cai, D. M. 2003. Resampling approach for anomaly detection in multispectral images. In Proceedings of the SPIE. vol. 5093, 230--240.

[326]

Thompson, B., II, R. M., Choi, J., El-Sharkawi, M., Huang, M., and Bunje, C. 2002. Implicit learning in auto-encoder novelty assessment. In Proceedings of the International Joint Conference on Neural Networks. 2878--2883.

[327]

Thottan, M. and Ji, C. 2003. Anomaly detection in IP networks. IEEE Trans. Sig. Proc. 51, 8, 2191--2204.

Digital Library

[328]

Tibshirani, R. and Hastie, T. 2007. Outlier sums for differential gene expression analysis. Biostatistics 8, 1, 2--8.

[329]

Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X. W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., Lee, C., Montie, J. E., Shah, R., Pienta, K. J., Rubin, M., and Chinnaiyan, A. M. 2005. Recurrent fusion of tmprss2 and ets transcription factor genes in prostate cancer. Science 310, 5748, 603--611.

[330]

Torr, P. and Murray, D. 1993. Outlier detection and motion segmentation. In Proceedings of the SPIE. Sensor Fusion VI, S. Schenker, Ed. vol. 2059. 432--443.

[331]

Tsay, R. S., Pea, D., and Pankratz, A. E. 2000. Outliers in multi-variate time series. Biometrika 87, 4, 789--804.

[332]

Vaidya, J. and Clifton, C. 2004. Privacy-preserving outlier detection. In Proceedings of the 4th IEEE International Conference on Data Mining. 233--240.

Digital Library

[333]

Valdes, A. and Skinner, K. 2000. Adaptive, model-based monitoring for cyber attack detection. In Proceedings of the 3rd International Workshop on Recent Advances in Intrusion Detection. Springer-Verlag, 80--92.

Digital Library

[334]

Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.

Digital Library

[335]

Vasconcelos, G., Fairhurst, M., and Bisset, D. 1994. Recognizing novelty in classification tasks. In Proceedings of the Neural Information Processing Systems Workshop on Novelty Detection and Adaptive Systems Monitoring.

[336]

Vasconcelos, G. C., Fairhurst, M. C., and Bisset, D. L. 1995. Investigating feed-forward neural networks with respect to the rejection of spurious patterns. Patt. Recog. Lett. 16, 2, 207--212.

Digital Library

[337]

Vilalta, R. and Ma, S. 2002. Predicting rare events in temporal domains. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 474.

Digital Library

[338]

Vinueza, A. and Grudic, G. 2004. Unsupervised outlier detection and semi-supervised learning. Tech. rep. CU-CS-976-04, University of Colorado at Boulder.

[339]

Wei, L., Qian, W., Zhou, A., and Jin, W. 2003. Hot: Hypergraph-based outlier test for categorical data. In Proceedings of the 7th Pacific-Asia Conference on Knowledge and Data Discovery. 399--410.

Digital Library

[340]

Weigend, A. S., Mangeas, M., and Srivastava, A. N. 1995. Nonlinear gated experts for time-series: Discovering regimes and avoiding overfitting. Int. J. Neural Syst. 6, 4, 373--399.

[341]

Weiss, G. M. and Hirsh, H. 1998. Learning to predict rare events in event sequences. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro, Eds. AAAI Press, 359--363.

[342]

Whitehead, B. and Hoyt, W. 1993. A function approximation approach to anomaly detection in propulsion system test data. In Proceedings of the 29th AIAA/SAE/ASME/ASEE Joint Propulsion Conference. IEEE Computer Society.

[343]

Williams, G., Baxter, R., He, H., Hawkins, S., and Gu, L. 2002. A comparative study of RNN for outlier detection in data mining. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 709.

Digital Library

[344]

Wong, W.-K., Moore, A., Cooper, G., and Wagner, M. 2002. Rule-based anomaly pattern detection for detecting disease outbreaks. In Proceedings of the 18th National Conference on Artificial Intelligence. MIT Press. http://www.cs.cmu.edu/~awm/antiterror.

Digital Library

[345]

Wong, W.-K., Moore, A., Cooper, G., and Wagner, M. 2003. Bayesian network anomaly pattern detection for disease outbreaks. In Proceedings of the 20th International Conference on Machine Learning. AAAI Press, 808--815.

[346]

Worden, K. 1997. Structural fault detection using a novelty measure. J. Sound Vibr. 201, 1, 85--101.

[347]

Wu, M. and Jermaine, C. 2006. Outlier detection by sampling with accuracy guarantees. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 767--772.

Digital Library

[348]

Wu, N. and Zhang, J. 2003. Factor analysis based anomaly detection. In Proceedings of the IEEE Workshop on Information Assurance. United States Military Academy.

[349]

Yairi, T., Kato, Y., and Hori, K. 2001. Fault detection by mining association rules from housekeeping data. In Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space.

[350]

Yamanishi, K. and ichi Takeuchi, J. 2001. Discovering outlier filtering rules from unlabeled data: Combining a supervised learner with an unsupervised learner. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 389--394.

Digital Library

[351]

Yamanishi, K., Takeuchi, J.-I., Williams, G., and Milne, P. 2004. Online unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min. Knowl. Disc. 8, 275--300.

Digital Library

[352]

Ye, N. and Chen, Q. 2001. An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems. Quality Reliability Engin. Int. 17, 105--112.

[353]

Yi, B.-K., Sidiropoulos, N., Johnson, T., Jagadish, H. V., Faloutsos, C., and Biliris, A. 2000. Online data mining for co-evolving time sequences. In Proceedings of the 16th International Conference on Data Engineering. IEEE Computer Society, 13.

Digital Library

[354]

Ypma, A. and Duin, R. 1998. Novelty detection using self-organizing maps. In Progress in Connectionist Based Information Systems. vol. 2. Springer, 1322--1325.

[355]

Yu, D., Sheikholeslami, G., and Zhang, A. 2002. Findout: Finding outliers in very large datasets. Knowl. Inform. Syst. 4, 4, 387--412.

Digital Library

[356]

Yu, J. X., Qian, W., Lu, H., and Zhou, A. 2006. Finding centric local outliers in categorical/numerical spaces. Knowl. Inform. Syst. 9, 3, 309--338.

Digital Library

[357]

Zeevi, A. J., Meir, R., and Adler, R. 1997. Time series prediction using mixtures of experts. In Advances in Neural Information Processing. vol. 9. MIT Press.

[358]

Zhang, J. and Wang, H. 2006. Detecting outlying subspaces for high-dimensional data: The new task, algorithms, and performance. Knowl. Inform. Syst. 10, 3, 333--355.

Digital Library

[359]

Zhang, K., Shi S., Gao, H., and Li, J. 2007. Unsupervised outlier detection in sensor networks using aggregation tree. In Advanced Data Mining and Applications 4632, 158--169.

Digital Library

[360]

Zhang, Z., Li, J., Manikopoulos, C., Jorgenson, J., and Ucles, J. 2001. Hide: A hierarchical network intrusion detection system using statistical preprocessing and neural network classification. In Proceedings of the IEEE Workshop on Information Assurance and Security. West Point, 85--90.

[361]

Zimmermann, J. and Mohay, G. 2006. Distributed intrusion detection in clusters based on non-interference. In Proceedings of the Australasian Workshops on Grid Computing and E-Research (ACSW Frontiers). Australian Computer Society, Inc., 89--95.

Digital Library

Cited By

Wang XAn YHu Q(2024)Anomaly prediction of Internet behavior based on generative adversarial networksPeerJ Computer Science10.7717/peerj-cs.200910(e2009)Online publication date: 23-Jul-2024
https://doi.org/10.7717/peerj-cs.2009
Arslan FJavaid ADanish Zaheer Awan MEbad-ur-Rehman (2024)Anomaly Detection in Time Series: Current Focus and Future ChallengesAnomaly Detection - Recent Advances, AI and ML Perspectives and Applications10.5772/intechopen.111886Online publication date: 17-Jan-2024
https://doi.org/10.5772/intechopen.111886
Fathi KWernher van de Venn H(2024)Data, Models, and Performance: A Comprehensive Guide to Predictive Maintenance in Industrial SettingsRecent Topics in Maintenance Management10.5772/intechopen.1005511Online publication date: 3-Jun-2024
https://doi.org/10.5772/intechopen.1005511
Show More Cited By

Index Terms

Anomaly detection: A survey
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As ...
Explainable contextual anomaly detection using quantile regression forests
Abstract
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a ...
Deep Learning for Anomaly Detection: A Review

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 41, Issue 3

July 2009

284 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1541880

Issue’s Table of Contents

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2009

Accepted: 01 May 2008

Revised: 01 March 2008

Received: 01 November 2007

Published in CSUR Volume 41, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7,338
Total Citations
View Citations
69,319
Total Downloads

Downloads (Last 12 months)5,898
Downloads (Last 6 weeks)647

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang XAn YHu Q(2024)Anomaly prediction of Internet behavior based on generative adversarial networksPeerJ Computer Science10.7717/peerj-cs.200910(e2009)Online publication date: 23-Jul-2024
https://doi.org/10.7717/peerj-cs.2009
Arslan FJavaid ADanish Zaheer Awan MEbad-ur-Rehman (2024)Anomaly Detection in Time Series: Current Focus and Future ChallengesAnomaly Detection - Recent Advances, AI and ML Perspectives and Applications10.5772/intechopen.111886Online publication date: 17-Jan-2024
https://doi.org/10.5772/intechopen.111886
Fathi KWernher van de Venn H(2024)Data, Models, and Performance: A Comprehensive Guide to Predictive Maintenance in Industrial SettingsRecent Topics in Maintenance Management10.5772/intechopen.1005511Online publication date: 3-Jun-2024
https://doi.org/10.5772/intechopen.1005511
Santos ECarr CLima AVieira AMelo Filho DDias ISilva R(2024)A utilização de aprendizado de máquina para a previsão do desmatamento no sudoeste do Pará: um estudo comparativoCuadernos de Educación y Desarrollo10.55905/cuadv16n6-06516:6(e4181)Online publication date: 13-Jun-2024
https://doi.org/10.55905/cuadv16n6-065
Chen CWang DMao FXu JZhang ZYu YDastani MSichman JAlechina NDignum V(2024)Deep Anomaly Detection via Active Anomaly SearchProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662879(308-316)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662879
Jin MHeizmann M(2024)Cutout as augmentation in contrastive learning for detecting burn marks in plastic granulesJournal of Sensors and Sensor Systems10.5194/jsss-13-63-202413:1(63-69)Online publication date: 4-Apr-2024
https://doi.org/10.5194/jsss-13-63-2024
Yilmaz ECan O(2024)Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat DetectionEngineering, Technology & Applied Science Research10.48084/etasr.691114:2(13341-13346)Online publication date: 2-Apr-2024
https://doi.org/10.48084/etasr.6911
AZZOUGUER DSEBAA AHADJOUT D(2024)Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical MethodsElectrotehnica, Electronica, Automatica10.46904/eea.23.72.2.110800672:2(54-62)Online publication date: 15-Jun-2024
https://doi.org/10.46904/eea.23.72.2.1108006
Baydargil HPark JInce I(2024)Anomaly‐based Alzheimer's disease detection using entropy‐based probability Positron Emission Tomography imagesETRI Journal10.4218/etrij.2023-012346:3(513-525)Online publication date: 22-Mar-2024
https://doi.org/10.4218/etrij.2023-0123
Niu LLi ZLi S(2024)MMD Fence GAN Unsupervised Anomaly Detection Model Based on Maximum Mean DiscrepancyInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.34481318:1(1-13)Online publication date: 22-May-2024
https://dl.acm.org/doi/10.4018/IJCINI.344813
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents