Attention based multi-agent intrusion detection systems using reinforcement learning

2021 ◽  
Vol 61 ◽  
pp. 102923
Author(s):  
Kamalakanta Sethi ◽  
Y. Venu Madhav ◽  
Rahul Kumar ◽  
Padmalochan Bera
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Multi Agent

scholarly journals DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems

Data in Brief ◽  
2021 ◽  
Vol 34 ◽  
pp. 106631
Author(s):  
Andrea Venturi ◽  
Giovanni Apruzzese ◽  
Mauro Andreolini ◽  
Michele Colajanni ◽  
Mirco Marchetti
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Benchmark Dataset ◽  
Intrusion Detection Systems ◽  
Detection Systems

scholarly journals A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

2021 ◽  
Vol 11 (2) ◽  
pp. 111-142
Author(s):  
Nassima Bougueroua ◽  
Smaine Mazouzi ◽  
Mohamed Belaoued ◽  
Noureddine Seddari ◽  
Abdelouahid Derhab ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection Systems ◽  
Multi Agent Systems ◽  
Research Issues ◽  
Detection Systems ◽  
Multi Agent ◽  
Collaborative Intrusion Detection ◽  
Complex Phenomena ◽  
General Architecture

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.

Disadvantages of Modern Intrusion Detection Systems

2006 ◽  
Vol 65 (10) ◽  
pp. 929-936
Author(s):  
A. V. Agranovskiy ◽  
S. A. Repalov ◽  
R. A. Khadi ◽  
M. B. Yakubets
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems
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