Improving Multi- Agent AI Model Based Distributed Intrusion Detection Systems

Abstract

Standard intrusion detection systems (IDS) are not sufficient for appropriate network security considering the increasing complexity and frequency of attacks. One significant advance are distributed intrusion detection systems (DIDS), which follow large, dispersed networks with scalability and agility. This paper explores approaches to enhance the multi-agent artificial intelligence (AI) models in DIDS performance. Multi-agent systems (MAS) provide real-time reactivity to complex cyber threats by means of distributed, cooperative, and autonomous detecting components. Important traits define their adaptability in changing threat situations, efficient use of resources, and ability to function in diverse network environments. This paper investigates MAS-driven DIDS design, implementation, and efficacy to underline their part in spotting and lowering known as well as novel attack routes. By means of machine learning, reinforcement learning, and cooperative algorithms, MAS increases DIDS's speed, accuracy, and robustness, hence increasing its capabilities. This work also addresses future directions in the usage of MAS-enhanced DIDS, implementation challenges, and pragmatic case studies.