Mix-attention approximation for homogeneous large-scale multi-agent reinforcement learning

Ensuring the security of computer networks is of utmost importance, and intrusion detection plays a vital role in safeguarding these systems. Traditional intrusion detection systems (IDSs) often suffer from drawbacks like reliance on outdated rules and centralized architectures, limiting their performance in the face of evolving threats and large-scale data networks. To address these challenges, we present an advanced anomaly detection-based IDS that utilizes a decentralized communicative multi-agent reinforcement learning (MARL). In our approach, multiple reinforcement learning agents collaborate in intrusion detection, effectively mitigating the non-stationarity problem and introducing a specialized secure communication method. We further enhance the learning process by incorporating external knowledge. Our approach is evaluated through extensive experiments conducted on the benchmark NSL Knowledge Discovery and Data Mining dataset. These experiments encompass diverse scenarios, involving varying numbers of agents to prove scalability feature. The results underscore the effectiveness of our method, which surpasses the performance of existing state-of-the-art solutions based on MARL, achieving a high accuracy rate of 97.80%.

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Mix-attention approximation for homogeneous large-scale multi-agent reinforcement learning

Cited by 2 publications

References 26 publications

Hierarchical multi-agent reinforcement learning for cooperative tasks with sparse rewards in continuous domain

Hierarchical multi-agent reinforcement learning for cooperative tasks with sparse rewards in continuous domain

An Intelligent Security System Using Enhanced Anomaly-Based Detection Scheme

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