Optimal Thresholds for Anomaly-Based Intrusion Detection in Dynamical Environments
In cyber-physical systems, malicious and resourceful attackers could penetrate a system through cyber means and cause significant physical damage. Consequently, early detection of such attacks becomes integral towards making these systems resilient to attacks. To achieve this objective, intrusion detection systems (IDS) that are able to detect malicious behavior early enough can be deployed. However, practical IDS are imperfect and sometimes they may produce false alarms even for normal system behavior. Since alarms need to be investigated for any potential damage, a large number of false alarms may increase the operational costs significantly. Thus, IDS need to be configured properly, as oversensitive IDS could detect attacks very early but at the cost of a higher number of false alarms. Similarly, IDS with very low sensitivity could reduce the false alarms while increasing the time to detect the attacks. The configuration of IDS to strike the right balance between time to detecting attacks and the rate of false positives is a challenging task, especially in dynamic environments, in which the damage caused by a successful attack is time-varying.
In this paper, using a game-theoretic setup, we study the problem of finding optimal detection thresholds for anomaly-based detectors implemented in dynamical systems in the face of strategic attacks. We formulate the problem as an attacker-defender security game, and determine thresholds for the detector to achieve an optimal trade-off between the detection delay and the false positive rates. In this direction, we first provide an algorithm that computes an optimal fixed threshold that remains fixed throughout. Second, we allow the detector’s threshold to change with time to further minimize the defender’s loss, and we provide a polynomial-time algorithm to compute time-varying thresholds, which we call adaptive thresholds. Finally, we numerically evaluate our results using a water-distribution network as a case study.
KeywordsCyber-physical systems Security Game theory Intrusion detection system
This work is supported in part by the the National Science Foundation (CNS-1238959), Air Force Research Laboratory (FA 8750-14-2-0180), National Institute of Standards and Technology (70NANB15H263), Office of Naval Research (N00014-15-1-2621), and by Army Research Office (W911NF-16-1-0069).
- 1.Abrams, M., Weiss, J.: Malicious control system cyber security attack case study - Maroochy Water Services, Australia, July 2008. http://csrc.nist.gov/groups/SMA/fisma/ics/documents/Maroochy-Water-Services-Case-Study_report.pdf
- 2.Alippi, C., Roveri, M.: An adaptive CUSUM-based test for signal change detection. In: Proceedings of the 2006 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 5752–5755. IEEE (2006)Google Scholar
- 3.Alpcan, T., Basar, T.: A game theoretic approach to decision and analysis in network intrusion detection. In: Proceedings of the 42nd IEEE Conference on Decision and Control (CDC), vol. 3, pp. 2595–2600. IEEE (2003)Google Scholar
- 4.Alpcan, T., Başar, T.: A game theoretic analysis of intrusion detection in access control systems. In: Proceedings of the 43rd IEEE Conference on Decision and Control (CDC), vol. 2, pp. 1568–1573. IEEE (2004)Google Scholar
- 6.Basseville, M., Nikiforov, I.V., et al.: Detection of Abrupt Changes: Theory and Application, vol. 104. Prentice Hall, Englewood Cliffs (1993)Google Scholar
- 7.Cárdenas, A.A., Amin, S., Lin, Z.-S., Huang, Y.-L., Huang, C.-Y., Sastry, S.: Attacks against process control systems: risk assessment, detection, and response. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security (ASIACCS), pp. 355–366. ACM (2011)Google Scholar
- 8.Casey, W., Morales, J.A., Nguyen, T., Spring, J., Weaver, R., Wright, E., Metcalf, L., Mishra, B.: Cyber security via signaling games: toward a science of cyber security. In: Natarajan, R. (ed.) ICDCIT 2014. LNCS, vol. 8337, pp. 34–42. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-04483-5_4 CrossRefGoogle Scholar
- 10.Estiri, M., Khademzadeh, A.: A theoretical signaling game model for intrusion detection in wireless sensor networks. In: Proceedings of the 14th International Telecommunications Network Strategy and Planning Symposium (NETWORKS), pp. 1–6. IEEE (2010)Google Scholar
- 14.Laszka, A., Abbas, W., Sastry, S.S., Vorobeychik, Y., Koutsoukos, X.: Optimal thresholds for intrusion detection systems. In: Proceedings of the 3rd Annual Symposium and Bootcamp on the Science of Security (HotSoS), pp. 72–81 (2016)Google Scholar
- 15.Laszka, A., Horvath, G., Felegyhazi, M., Buttyan, L., FlipThem: modeling targeted attacks with FlipIt for multiple resources. In: Proceedings of the 5th Conference on Decision and Game Theory for Security (GameSec), pp. 175–194, November 2014Google Scholar
- 17.Lee, R.M., Assante, M.J., Conway, T.: German steel mill cyber attack. Technical report, SANS Industrial Control Systems (2014)Google Scholar
- 19.Pawlick, J., Farhang, S., Zhu, Q.: Flip the cloud: cyber-physical signaling games in the presence of advanced persistent threats. In: Khouzani, M.H.R., Panaousis, E., Theodorakopoulos, G. (eds.) GameSec 2015. LNCS, vol. 9406, pp. 289–308. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25594-1_16 CrossRefGoogle Scholar
- 23.Tantawy, A.M.: Model-based detection in cyber-physical systems. Ph.D. thesis, Vanderbilt University (2011)Google Scholar