AgenticCyber：一种基于生成式人工智能的多智能体系统，用于网络安全中的多模态威胁检测与自适应响应 (AgenticCyber: A GenAI-Powered Multi-Agent System for Multimodal Threat Detection and Adaptive Response in Cybersecurity)

The increasing complexity of cyber threats in distributed environments demands advanced frameworks for real-time detection and response across multimodal data streams. This paper introduces AgenticCyber, a generative AI powered multi-agent system that orchestrates specialized agents to monitor cloud logs, surveillance videos, and environmental audio concurrently. The solution achieves 96.2% F1-score in threat detection, reduces response latency to 420 ms, and enables adaptive security posture management using multimodal language models like Google's Gemini coupled with LangChain for agent orchestration. Benchmark datasets, such as AWS CloudTrail logs, UCF-Crime video frames, and UrbanSound8K audio clips, show greater performance over standard intrusion detection systems, reducing mean time to respond (MTTR) by 65% and improving situational awareness. This work introduces a scalable, modular proactive cybersecurity architecture for enterprise networks and IoT ecosystems that overcomes siloed security technologies with cross-modal reasoning and automated remediation.

翻译：分布式环境中网络威胁日益复杂，亟需能够跨多模态数据流进行实时检测与响应的先进框架。本文提出AgenticCyber，一种由生成式人工智能驱动的多智能体系统，该系统协调多个专用智能体，同时监控云日志、监控视频和环境音频。该解决方案在威胁检测中实现了96.2%的F1分数，将响应延迟降低至420毫秒，并通过结合Google的Gemini等多模态语言模型与LangChain进行智能体编排，实现了自适应安全态势管理。在AWS CloudTrail日志、UCF-Crime视频帧和UrbanSound8K音频片段等基准数据集上的测试表明，其性能优于标准入侵检测系统，将平均响应时间（MTTR）降低了65%，并提升了态势感知能力。本研究提出了一种可扩展、模块化的主动式网络安全架构，适用于企业网络和物联网生态系统，通过跨模态推理与自动化修复，克服了孤立安全技术的局限。