Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Towards multisensor data fusion for DoS detection
Proceedings of the 2004 ACM symposium on Applied computing
Autonomous Smart Routing for Network QoS
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
D-WARD: A Source-End Defense against Flooding Denial-of-Service Attacks
IEEE Transactions on Dependable and Secure Computing
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Defending DDoS attacks using hidden Markov models and cooperative reinforcement learning
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Multi-agent reinforcement learning for intrusion detection
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
A reinforcement learning approach for host-based intrusion detection using sequences of system calls
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Cooperative multiagent congestion control for high-speed networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper we propose a novel approach to train Multi-Agent Reinforcement Learning(MARL) agents to cooperate to detect intrusions in the form of normal and abnormal states in the network. We present an architecture of distributed sensor and decision agents that learn how to identify normal and abnormal states of the network using Reinforcement Learning(RL). Sensor agents extract network-state information using tile-coding as a function approximation technique and send communication signals in the form of actions to decision agents. By means of an on line process, sensor and decision agents learn the semantics of the communication actions. In this paper we detail the learning process and the operation of the agent architecture. We also present tests and results of our research work in an intrusion detection case study, using a realistic network simulation where sensor and decision agents learn to identify normal and abnormal states of the network.