Learning in multiagent systems
Multiagent systems
Intrusion detection systems and multisensor data fusion
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Reinforcement Learning for 3 vs. 2 Keepaway
RoboCup 2000: Robot Soccer World Cup IV
Experience with EMERALD to Date
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Towards multisensor data fusion for DoS detection
Proceedings of the 2004 ACM symposium on Applied computing
A taxonomy of DDoS attack and DDoS defense mechanisms
ACM SIGCOMM Computer Communication Review
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Journal of Artificial Intelligence Research
Multi-Agent Reinforcement Learning for Intrusion Detection: A Case Study and Evaluation
MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Review: Multi-agent systems for protecting critical infrastructures: A survey
Journal of Network and Computer Applications
An orchestration approach for unwanted Internet traffic identification
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Intrusion Detection Systems (IDS) have been investigated for many years and the field has matured. Nevertheless, there are still important challenges, e.g., how an IDS can detect new and complex distributed attacks. To tackle these problems, we propose a distributed Reinforcement Learning (RL) approach in a hierarchical architecture of network sensor agents. Each network sensor agent learns to interpret local state observations, and communicates them to a central agent higher up in the agent hierarchy. These central agents, in turn, learn to send signals up the hierarchy, based on the signals that they receive. Finally, the agent at the top of the hierarchy learns when to signal an intrusion alarm. We evaluate our approach in an abstract network domain.