A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Author:

Bougueroua Nassima1,Mazouzi Smaine1,Belaoued Mohamed1,Seddari Noureddine1,Derhab Abdelouahid2,Bouras Abdelghani3

Affiliation:

1. Department of Computer Science , 20 August 1955 University of Skikda , Algeria

2. Center of Excellence in Information Assurance (COEIA) , King Saud University , Riyadh , Saudi Arabia

3. Department of Industrial Engineering , Alfaisal University , Riyadh , Saudi Arabia

Abstract

Abstract Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modelling and Simulation,Information Systems

Reference202 articles.

1. [1] F. Abdoli and M. Kahani. Ontology-based distributed intrusion detection system. In 2009 14th International CSI Computer Conference, pages 65–70. IEEE, oct 2009.

2. [2] Yuehui. ABRAHAM, Ajith; GROSAN, Crina; et CHEN. Cyber security and the evolution in intrusion detection systems. Journal of Engineering and Technology, pages 0973–2632, 2005.

3. [3] Abdulla Amin Aburomman and Mamun Bin Ibne Reaz. Survey of learning methods in intrusion detection systems. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), pages 362–365. IEEE, nov 2016.

4. [4] Omar Achbarou, My Ahmed El Kiram, Outmane Bourkoukou, and Salim Elbouanani. A New Distributed Intrusion Detection System Based on Multi-Agent System for Cloud Environment. International Journal of Communication Networks and Information Security (IJCNIS), 10(3):2018, 2018.

5. [5] Neda Afzali Seresht and Reza Azmi. MAISIDS: A distributed intrusion detection system using multi-agent AIS approach. Engineering Applications of Artificial Intelligence, 35:286–298, oct 2014.

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