Abstract
Advanced Persistent Threats (APTs) are considered as the threats that are the most challenging to detect and defend against. As APTs use sophisticated attack methods, cyber situational awareness and especially cyber attack attribution are necessary for the preservation of security of cyber infrastructures. Recent challenges faced by organizations in the light of APT proliferation are related to the: collection of APT knowledge; monitoring of APT activities; detection and classification of APTs; and correlation of all these to result in the attribution of the malicious parties that orchestrated an attack. We propose the Enhanced Cyber Attack Attribution (NEON) Framework, which performs attribution of malicious parties behind APT campaigns. NEON is designed to increase societal resiliency to APTs. NEON combines the following functionalities: (i) data collection from APT campaigns; (ii) collection of publicly available data from social media; (iii) honeypots and virtual personas; (iv) network and system behavioural monitoring; (v) incident detection and classification; (vi) network forensics; (vii) dynamic response based on game theory; and (viii) adversarial machine learning; all designed with privacy considerations in mind.
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References
Farinholt, B., et al.: To catch a ratter: monitoring the behavior of amateur DarkComet RAT operators in the wild. In: IEEE Symposium on Security and Privacy, pp. 770–787. IEEE (2017)
Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.: Adversarial machine learning. In: 4th ACM Workshop on Security and Artificial Intelligence, pp. 43–58. ACM (2011)
Pfleeger, S.L., Sasse, M.A., Furnham, A.: From weakest link to security hero: transforming staff security behavior. J. Homel. Secur. Emerg. Manag. 11(4), 489–510 (2014)
Langner, R.: Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur. Priv. 9(3), 49–51 (2011)
Kaspersky: Targeted cyber attacks logbook. https://apt.securelist.com/. Accessed 09 Feb 2018
Symantec: Advanced persistent threats: a symantec perspective. https://www.symantec.com/content/en/us/enterprise/white_papers/b-advanced_persistent_threats_WP_21215957.en-us.pdf. Accessed 09 Feb 2018
ITU: Targeted attack trends. https://www.itu.int/en/ITU-D/Cybersecurity/Documents/2H_2013_Targeted_Attack_Campaign_Report.pdf. Accessed 09 Feb 2018
King, S.: Apt (advanced persistent threat) - what you need to know. https://www.netswitch.net/apt-advanced-persistent-threat-what-you-need-to-know/. Accessed 09 Feb 2018
Cavelty, M.D.: Cyber-security and Threat Politics: US Efforts to Secure the Information Age. Routledge, Abingdon (2007)
Choo, K.K.R.: The cyber threat landscape: challenges and future research directions. Comput. Secur. 30(8), 719–731 (2011)
Giura, P., Wang, W.: A context-based detection framework for advanced persistent threats. In: International Conference on Cyber Security, pp. 69–74. IEEE (2012)
Virvilis, N., Gritzalis, D.: The big four-what we did wrong in advanced persistent threat detection? In: 8th International Conference on Availability, Reliability and Security, pp. 248–254. IEEE (2013)
Jasek, R., Kolarik, M., Vymola, T.: APT detection system using honeypots. In: 13th International Conference on Applied Informatics and Communications, pp. 25–29. (2013)
Chen, P., Desmet, L., Huygens, C.: A study on advanced persistent threats. In: De Decker, B., Zúquete, A. (eds.) CMS 2014. LNCS, vol. 8735, pp. 63–72. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44885-4_5
Friedberg, I., Skopik, F., Settanni, G., Fiedler, R.: Combating advanced persistent threats: from network event correlation to incident detection. Comput. Secur. 48, 35–57 (2015)
Marchetti, M., Pierazzi, F., Colajanni, M., Guido, A.: Analysis of high volumes of network traffic for advanced persistent threat detection. Comput. Netw. 109, 127–141 (2016)
Hu, P., Li, H., Fu, H., Cansever, D., Mohapatra, P.: Dynamic defense strategy against advanced persistent threat with insiders. In: IEEE Conference on Computer Communications, pp. 747–755. IEEE (2015)
Zhu, Q., Rass, S.: On multi-phase and multi-stage game-theoretic modeling of advanced persistent threats. IEEE Access 6, 13958–13971 (2018)
Bhatt, P., Yano, E.T., Gustavsson, P.: Towards a framework to detect multi-stage advanced persistent threats attacks. In: 2014 IEEE 8th International Symposium on Service Oriented System Engineering (SOSE), pp. 390–395. IEEE (2014)
Giura, P., Wang, W.: Using large scale distributed computing to unveil advanced persistent threats. Sci. J. 1(3), 93–105 (2012)
Wheeler, D.A., Larsen, G.N.: Techniques for cyber attack attribution. Technical report, Institute for Defense Analyses, Alexandria, VA (2003)
Hunker, J., Hutchinson, B., Margulies, J.: Role and challenges for sufficient cyber-attack attribution. Institute for Information Infrastructure Protection, pp. 5–10 (2008)
Bou-Harb, E., Lucia, W., Forti, N., Weerakkody, S., Ghani, N., Sinopoli, B.: Cyber meets control: a novel federated approach for resilient CPS leveraging real cyber threat intelligence. IEEE Commun. Mag. 55(5), 198–204 (2017)
Qamar, S., Anwar, Z., Rahman, M.A., Al-Shaer, E., Chu, B.T.: Data-driven analytics for cyber-threat intelligence and information sharing. Comput. Secur. 67, 35–58 (2017)
DARPA: Enhanced attribution federal project. https://govtribe.com/project/enhanced-attribution. Accessed 09 Feb 2018
Kintis, P., et al.: Hiding in plain sight: a longitudinal study of combosquatting abuse. In: ACM Conference on Computer and Communications Security, pp. 569–586. ACM (2017)
Keromytis, A.: Enhanced attribution. https://www.enisa.europa.eu/events/cti-eu-event/cti-eu-event-presentations/enhanced-attribution/. Accessed 09 Feb 2018
David Westcott, K.B.: Aptnotes. https://github.com/aptnotes/data. Accessed 09 Feb 2018
Meusel, R., Mika, P., Blanco, R.: Focused crawling for structured data. In: 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1039–1048. ACM (2014)
Triguero, I., GarcÃa, S., Herrera, F.: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl. Inf. Syst. 42(2), 245–284 (2015)
Olston, C., Najork, M.: Web crawling. Found. Trends Inf. Retr. 4(3), 175–246 (2010)
Cimiano, P.: Ontology learning from text. In: Cimiano, P. (ed.) Ontology Learning and Population from Text: Algorithms, Evaluation and Applications, pp. 19–34. Springer, Boston (2006). https://doi.org/10.1007/978-0-387-39252-3_3
Gialampoukidis, I., Moumtzidou, A., Tsikrika, T., Vrochidis, S., Kompatsiaris, I.: Retrieval of multimedia objects by fusing multiple modalities. In: ACM on International Conference on Multimedia Retrieval, pp. 359–362. ACM (2016)
Pitropakis, N., Pikrakis, A., Lambrinoudakis, C.: Behaviour reflects personality: detecting co-residence attacks on Xen-based cloud environments. Int. J. Inf. Secur. 14(4), 299–305 (2015)
Davidoff, S., Ham, J.: Network Forensics: Tracking Hackers Through Cyberspace, vol. 2014. Prentice Hall, Upper Saddle River (2012)
Fielder, A., Panaousis, E., Malacaria, P., Hankin, C., Smeraldi, F.: Decision support approaches for cyber security investment. Decis. Support Syst. 86, 13–23 (2016)
Fielder, A., Panaousis, E., Malacaria, P., Hankin, C., Smeraldi, F.: Game theory meets information security management. In: Cuppens-Boulahia, N., Cuppens, F., Jajodia, S., Abou El Kalam, A., Sans, T. (eds.) SEC 2014. IAICT, vol. 428, pp. 15–29. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55415-5_2
Fielder, A., Konig, S., Panaousis, E., Schauer, S., Rass, S.: Uncertainty in cyber security investments. arXiv preprint arXiv:1712.05893 (2017)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)
Nikhi, B., Giannetsos, T., Panaousis, E., Took, C.C.: Unsupervised learning for trustworthy IoT. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2018)
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Pitropakis, N., Panaousis, E., Giannakoulias, A., Kalpakis, G., Rodriguez, R.D., Sarigiannidis, P. (2018). An Enhanced Cyber Attack Attribution Framework. In: Furnell, S., Mouratidis, H., Pernul, G. (eds) Trust, Privacy and Security in Digital Business. TrustBus 2018. Lecture Notes in Computer Science(), vol 11033. Springer, Cham. https://doi.org/10.1007/978-3-319-98385-1_15
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DOI: https://doi.org/10.1007/978-3-319-98385-1_15
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