Abstract
Multi-agent architectures have been able to attract a lot of interest from academics. This is due to their proven capabilities, such as autonomy, integrated intelligence, learning and autonomous knowledge, high scalability and tolerance to faults, etc. Through this technology, the development of ambient safety systems has become de facto standard in order to meet the dynamic and open essence of today’s internet resources. Even though multi-agent architectures are more and more explored in computer security, the performance of intrusions and attack detection is not sufficiently demonstrated by empirical evidence. A deep multi-agent architecture based on learning is suggested in this paper that can recognize and create a specification level alarm. The review of the results reveals an improvement over previous work.
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Jain, T., Gupta, C. (2022). Multi-Agent Intrusion Detection System Using Sparse PSO K-Mean Clustering and Deep Learning. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_10
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DOI: https://doi.org/10.1007/978-981-16-6332-1_10
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