MULTI-AGENT DEFENSE SYSTEMS AND THEIR EFFECTIVENESS EVALUATION
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| Natura: | Recurso digital |
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Zenodo
2025
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| author | Kurbonaliyeva Dilshoda Vali kizi |
| author_facet | Kurbonaliyeva Dilshoda Vali kizi |
| contents | <p>This paper explores the effectiveness of Multi-Agent Systems (MAS) in modern cybersecurity infrastructures.<br>As traditional centralized models struggle to address dynamic and complex threats, MAS provides a distributed and<br>autonomous alternative. The study evaluates real-world MAS implementations such as Suricata, HoneyMesh, Google<br>DC Agents, AWS IoT Defender, Azure Sphere, Tesla FSD, and IBM QRadar-Watson using three key indicators: response<br>time, detection accuracy, and overall efciency.<br>Findings show that agent-based architectures enhance system resilience, enable real-time threat analysis, and mitigate<br>single points of failure. Integration with artifcial intelligence (AI) and federated learning further improves predictive<br>capabilities. MAS also supports proactive defense, adaptive coordination, and efcient resource utilization, making them<br>ideal for securing IoT environments, edge computing systems, and future 6G networks. The results highlight MAS as a<br>strategic solution for building flexible, scalable, and intelligent cybersecurity frameworks capable of addressing evolving<br>digital threats.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17436770 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MULTI-AGENT DEFENSE SYSTEMS AND THEIR EFFECTIVENESS EVALUATION Kurbonaliyeva Dilshoda Vali kizi <p>This paper explores the effectiveness of Multi-Agent Systems (MAS) in modern cybersecurity infrastructures.<br>As traditional centralized models struggle to address dynamic and complex threats, MAS provides a distributed and<br>autonomous alternative. The study evaluates real-world MAS implementations such as Suricata, HoneyMesh, Google<br>DC Agents, AWS IoT Defender, Azure Sphere, Tesla FSD, and IBM QRadar-Watson using three key indicators: response<br>time, detection accuracy, and overall efciency.<br>Findings show that agent-based architectures enhance system resilience, enable real-time threat analysis, and mitigate<br>single points of failure. Integration with artifcial intelligence (AI) and federated learning further improves predictive<br>capabilities. MAS also supports proactive defense, adaptive coordination, and efcient resource utilization, making them<br>ideal for securing IoT environments, edge computing systems, and future 6G networks. The results highlight MAS as a<br>strategic solution for building flexible, scalable, and intelligent cybersecurity frameworks capable of addressing evolving<br>digital threats.</p> |
| title | MULTI-AGENT DEFENSE SYSTEMS AND THEIR EFFECTIVENESS EVALUATION |
| url | https://doi.org/10.5281/zenodo.17436770 |