+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|Title: ||Adding contextual information to intrusion detection systems using fuzzy cognitive maps|
|Authors: ||Aparicio-Navarro, Francisco J.|
Kyriakopoulos, Konstantinos G.
Parish, David J.
|Keywords: ||Basic probability assignment|
Fuzzy cognitive maps
Intrusion detection systems
|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|
|Publisher Link: ||http://dx.doi.org/10.1109/COGSIMA.2016.7497807|
|Appears in Collections:||Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.