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Title: Adding contextual information to intrusion detection systems using fuzzy cognitive maps
Authors: Aparicio-Navarro, Francisco J.
Kyriakopoulos, Konstantinos G.
Parish, David J.
Chambers, Jonathon
Keywords: Basic probability assignment
Contextual information
Dempster-Shafer theory
Fuzzy cognitive maps
Intrusion detection systems
Network security
Issue Date: 2016
Publisher: © IEEE
Citation: APARICIO-NAVARRO, F. ... et al., 2016. Adding contextual information to intrusion detection systems using fuzzy cognitive maps. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, USA, 20-25 March 2016, pp. 180 - 186.
Abstract: In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information from the network users and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections.
Description: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the MOD University Defence Research Collaboration in Signal Processing.
Version: Accepted for publication
DOI: 10.1109/COGSIMA.2016.7497807
URI: https://dspace.lboro.ac.uk/2134/20949
Publisher Link: http://dx.doi.org/10.1109/COGSIMA.2016.7497807
ISBN: 9781509006311
ISSN: 2379-1675
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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