MULTI-AGENT INTRUSION DETECTION AND PREVENTION SYSTEMS (IDPS) IN CYBERSECURITY: ARCHITECTURES, BENCHMARKS, AND METHODOLOGICAL MITIGATION
DOI:
https://doi.org/10.5281/zenodo.20355491Keywords:
Multi-Agent Systems, Intrusion Detection, Distributed Computing, Edge-AI, CSE-CIC-IDS2018, Cyber Telemetry.Abstract
The exponential scaling and increasing heterogeneity of contemporary cloud infrastructures, Internet of Things (IoT) ecosystems, and distributed corporate networks have exposed severe architectural limitations in centralized Intrusion Detection and Prevention Systems (IDPS). Single-point bottlenecks, high alert triage latency, and systemic vulnerability to zero-day coordinated adversarial vectors necessitate a paradigm shift toward distributed computational defenses. Multi-Agent Intrusion Detection and Prevention Systems (MA-IDPS) present a modular framework where localized, specialized software entities autonomously sense, analyze, and collaboratively neutralize threat vectors across network perimeters. This article concludes with an analytical matrix juxtaposing current deployment strategies to furnish security architects with clear, resource-optimized guidelines for heterogeneous cloud infrastructures.References
Alshahwan, F., & Al-Sarkhi, A. (2023). Multi-Agent Systems for Distributed Security: A Review of Modern IDPS Frameworks. MDPI Systems and Infrastructure Security, 4(1), 45-62. https://www.mdpi.com/2624-800X/4/1/45
Anonymous Authors. (2024). Decentralized Cyber Telemetry Isolation using Intelligent Autonomous Software Entities. Journal of Cloud Security Assurance, 12(3), 112-128.
Boutet, L., Rachid, M., & Vance, J. (2024). Reinforcement Learning in Collaborative Agent Networks for Zero-Day Attack Abatement. In Proceedings of the 2024 IEEE International Conference on Cyber-Physical Systems (ICCPS), 89-102. https://doi.org/10.1109/ICCPS.2024.00014
Zhang, Y., & El-Amir, M. (2025). Hierarchical Multi-Agent Network Hardening: Autoencoder Deployment at the Enterprise Edge. IEEE Transactions on Network and Service Management, 22(2), 1420-1433. https://doi.org/10.1109/TNSM.2025.14203
Communications Security Establishment (CSE) & Canadian Institute for Cybersecurity (CIC). (2023). Comprehensive Evaluation of Machine Learning Paradigms on the CSE-CIC-IDS2018 Network Threat Dataset. Government Cyber Security Analytics Reports, 14(2), 201-215.
Edge-IIoTset Consortium. (2024). Industrial Internet of Things Cyber-Attack Benchmarks for Decentralized Machine Learning Deployments. IEEE Security & Privacy, 22(4), 34-45.
Gomez, F., Martinez, S., & Tuan, N. (2025). Vulnerability Analysis of FIPA-ACL Communication Frameworks under Leaf Agent Exploits. arXiv preprint, arXiv:2501.09841. https://doi.org/10.48550/arxiv.2501.09841
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Innovative Academy RSC

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite