Lookup NU author(s): Dr Francisco Aparicio Navarro,
Professor Jonathon Chambers
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
As the complexity of cyber-attacks keeps increasing, new 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 this end, we make use of the pattern-of-life (PoL) of a computer network as the main source of high-level information. We propose two novel approaches that make use of a Fuzzy cognitive map (FCM) to incorporate the PoL into the detection process. There are four main aims of the work. First, to evaluate the efficiency of the proposed approaches in identifying the presence of attacks. Second, to identify which of the proposed approaches to integrate an FCM into the IDS framework produces the best results. Third, to identify which of the metrics used in the design of the FCM produces the best detection results. Fourth, to evidence the improved detection performance that contextual information can offer in IDSs. The results that we present verify that the proposed approaches improve the effectiveness of our IDS by reducing the total number of false alarms; providing almost perfect detection rate (i.e., 99.76%), and only 6.33% false positive rate, depending on the particular metric combination.
Author(s): Aparicio-Navarro FJ, Kyriakopoulos KG, Gong Y, Parish DJ, Chambers JA
Publication type: Article
Publication status: Published
Journal: IEEE Access
Print publication date: 22/10/2017
Acceptance date: 16/09/2017
Date deposited: 23/10/2017
ISSN (electronic): 2169-3536
Data Source Location: https://figshare.com/s/4bd0fe2dab7e09ce61dc
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