Abstract
Twitter is one of the most popular social networking and micro-blogging Web sites in the world. TinyURL is a URL or link shortening web service used by Twitter. Recently, it is being exploited by spammers as a podium to transmit malicious information. TinyURL spam detection in Twitter is a challenging task. In this paper, an efficient scheme is proposed to detect spam in the TinyURL with particular focus on Twitter tweets. A set of features are first extracted from the tweets. This feature set is analyzed to select a reduced set of features. The reduced feature set is fed as input to train three classifiers, namely simple logistic regression, decision tree, and SVM. The classification results show that the SVM classifier has the highest accuracy in detecting spam in TinyURLs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jin, L., Chen, Y., Wang, T.G., Hui, P., Vasilakos, A.V.: Understanding user behavior in online social networks: A survey. IEEE Commun. Mag. 51(9), 144–150 (2013)
Kim, B.I., Im, C.T., Jung, H.C.: Suspicious malicious web site detection with strength analysis of a javascript obfuscation. Int. J. Adv. Sci. Technol. 26, 19–32 (2011)
Ma, J., Saul, K.L., Savage, S., Geoffrey, M.V.: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1245–1254. https://doi.org/10.1145/1557019.1557153 (2009)
Irani, D., Webb, S., Pu, C.: Study of static classification of social spam profiles in myspace. In: Fourth International AAAI Conference on Weblogs and Social Media, May 23–26, pp. 82–89. George Washington University, DC (2010)
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honey pots þ machine learning. In: 33rd Int’l ACM SIGIR Conference Research and Development in Information Retrieval, 978-1-60558-896-4/10/07 (2010)
Wang, A.H., Don’t Follow me: spam detecting in Twitter. In: International Conference on Security and Cryptography, Athens, Greece, pp. 1–10 (2010)
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Seventh Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS) July, Redmond, Washington, USA (2010)
Strinhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks, In: 26th Annual Computer Security Applications Conference (ACSAC), pp. 1–9 (2015)
Song, J., Lee, S., Kim, J.: Spam filtering in Twitter using sender-receiver relationship. In: 14th International Conference on Recent Advances in Intrusion Detection (RAID), pp. 301–317. Menlo Park, CA, 20–21 Sept 2011
Gao, H., Chen, H., Lee, K., Palsetia, D., Choudhary, A.: Towards online spam filtering in social networks. In: 19th Annual Network and Distributed System Security Symposium. Hilton, San Diego, USA, 5–8 Feb 2012
Klein, F., Strohmaier, M.: Short links under attack: geographical analysis of spam in a URL shortener network. In: 23rd ACM Conference on Hypertext and Social Media. Milwaukee, WI, USA, 25–28 June 2012
Ameya, S., Joyce, J., Upadhyaya, S.: The early bird spreads the worm: an assessment of Twitter for malware propagation. Proced. Comput. Sci. 10(3), 705–7121 (2012)
Moshchuk, A., Bragin, T., Gribble, S., Levy, H.: A crawler-based study of spyware in the web. In: Proceedings of the Symposium on Network and Distributed System Security (NDSS) (2006)
Provos, N., Mavrommatis, P., Rajab, M.A., Monrose, F.: All your iFrames point to us. In: 17th Conference on Security Symposium, San Jose, CA, July 28–Aug 01, pp. 1–15 (2008)
Ikinci, A., Holz, T., Freiling, F.: Monkey-spider: detecting malicious websites with low-interaction honey clients. In: Proceedings of Sicherheit, Schutz und Zuverlässigkeit, 2–4 Apr 2008
Eckersley, P.: How unique is your web browser? In: 10th International Conference in Privacy Enhancing Technologies (PET), pp. 1–18. Berlin, Germany, 21–23 July 2010
Kapravelos, A., Cova, M., Kruegel, C., Vigna, G.: Escape from monkey Island: evading high-interaction honey clients. In: Holz, T., Bos, H. (eds.) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA, Lecture Notes in Computer Science, vol. 6739. Springer, Berlin, Heidelberg (2011)
Zhang, J., Seifert, C., Stokes, J.W., Lee, W.: ARROW: generating signatures to detect drive-by downloads. In: 20th International World Wide Web Conference, pp. 187–196. New York, USA, ACM (2011)
Lee, S., Kim, J.: Warning bird, a near real time system for a suspicious Twitter stream. IEEE Trans. Depend. Sec. Comput. 10(3), 157–165 (2013)
Vangapandu, K.B., Brewer, D., Li, K.: A study of URL redirection indicating spam. In: CEAS Sixth Conference on Email and Anti Spam, Mountain View, California, USA, 16–17 July 2009
Kamoru, B.A., Jaafar, A., Jabar, M.A.B., Murad, M.A.A.: A mapping study to investigate spam detection on social networks. Int. J. Appl. Inf. Syst. 11(11), 16–34 (2017)
Miller, Z., Dickinson, B., Deitrick, W., Hu, W., Wang, A.H.: Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2013)
Feinstein, B., Peck, D.: Caffeine monkey: automated collection, detection and analysis of malicious javascript. In: Proceedings of the Black Hat Security Conference. Las Vegas, NV, USA (2007)
Canali, D., Cova, M., Vigna, G., Kruegel, C.: Prophiler: a fast filter for the large-scale detection of malicious web pages. In: 20th International World Wide Web Conference, pp. 197–206. Hyderabad, India, March 28–April 1 (2011)
McGrath, D.K., Gupta, M.: Behind phishing: an examination of Phisher Modi Operandi. In: Of the USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET), San Francisco, CA (2008)
Moura, G.C.M., Sadre, R., Pras, A.: Taking on internet bad neighborhoods. IEEE Symp. Netw. Oper. Manag. (NOMS) 5–9(May), 1–7 (2014)
Page, L., Brin, S., Motwami, R., Winograd, T.: The page rank citation ranking: bringing order to the web, technical report. Computer Science Department, Stanford University (1999)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Padmanabhan, S., Maramreddy, P., Cyriac, M. (2020). Spam Detection in Link Shortening Web Services Through Social Network Data Analysis. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-1097-7_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1096-0
Online ISBN: 978-981-15-1097-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)