Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems


As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination.


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


Contextual Information, Dempster-Shafer Theory, Fuzzy Cognitive Maps, Intrusion Detection Systems, Network Security, Pattern-of-Life, Port Scanning Attack


Aparicio-Navarro, F.J., Chambers, J.A., Kyriakopoulos, K., Gong, Y., Parish, D.J. (2017) Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems. Proceedings of IEEE International Conference on Communications (ICC), Paris, France, July 2017.


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