Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System

Hasanen Alyasiri; John Clark; Daniel Kudenko

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Paper

Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System

Topics: Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance; Applications: Image and Signal Processing, Pattern Recognition, Decision Making, Human-Robot Interaction, Human Computer Interaction, Cognitive Robotics and Developmental Robotic, Sensor Mesh, Intelligent Networks, Internet Modeling, Multi-sensor Data Fus

In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 0IJCCI, 176-183, 2018 , Seville, Spain

Authors: Hasanen Alyasiri ¹ ; John Clark ² and Daniel Kudenko ¹

Affiliations: ¹ Department of Computer Science, University of York and U.K. ; ² Department of Computer Science, University of Sheffield and U.K.

Keyword(s): Cartesian Genetic Programming, Intrusion Detection System, Stacking Ensemble.

Abstract: With cyber-attacks becoming a regular feature in daily business and attackers continuously evolving their techniques, we are witnessing ever more sophisticated and targeted threats. Various artificial intelligence algorithms have been deployed to analyse such incidents. Extracting knowledge allows the discovery of new attack methods, intrusion scenarios, and attackers’ objectives and strategies, all of which can help distinguish attacks from legitimate behaviour. Among those algorithms, Evolutionary Computation (EC) techniques have seen significant application. Research has shown it is possible to utilize EC methods to construct IDS detection rules. In this paper, we show how Cartesian Genetic Programming (CGP) can construct the behaviour rule upon which an intrusion detection will be able to make decisions regarding the nature of the activity observed in the system. The CGP framework evolves human readable solutions that provide an explanation of the logic behind its evolved decisio ns. Experiments are conducted on up-to-date cybersecurity datasets and compared with state of the art paradigms. We also introduce ensemble learning paradigm, indicating how CGP can be used as stacking technique to improve the learning performance. (More)

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Paper citation in several formats:

Alyasiri, H., Clark, J. and Kudenko, D. (2018). Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System. In Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI; ISBN 978-989-758-327-8; ISSN 2184-3236, SciTePress, pages 176-183. DOI: 10.5220/0006925901760183

@conference{ijcci18,
author={Hasanen Alyasiri and John Clark and Daniel Kudenko},
title={Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI},
year={2018},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006925901760183},
isbn={978-989-758-327-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI
TI - Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System
SN - 978-989-758-327-8
IS - 2184-3236
AU - Alyasiri, H.
AU - Clark, J.
AU - Kudenko, D.
PY - 2018
SP - 176
EP - 183
DO - 10.5220/0006925901760183
PB - SciTePress