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English(EN) Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

新型HMAS系统提供成本效益高的勒索软件检测

研究人员开发了一种成本感知型分层多智能体系统(HMAS),用于更高效的勒索软件检测和家族归因。该系统采用专业智能体的分层组织,由元协调器进行协调,以自适应地选择分析模式。它优先使用低成本的静态分析,并仅在必要时选择性地使用动态和内存分析,由一个平衡性能与计算成本的成本模型指导。实验表明,HMAS在检测和归因方面实现了高准确率,同时与详尽方法相比,分析成本降低了近44%,其中很大一部分案例仅使用静态证据即可解决。 AI

影响 这项研究可能带来更高效、更具成本效益的网络安全工具,用于检测和分类勒索软件威胁。

排序理由 详细介绍一种新型勒索软件检测系统的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型HMAS系统提供成本效益高的勒索软件检测

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详细介绍一种新型勒索软件检测系统的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mubashar Iqbal, Asifullah Khan ·

    成本感知式分层多智能体勒索软件检测与家族归属

    arXiv:2609.04820v1 Announce Type: cross Abstract: Ransomware detection and family attribution require analysis of different modalities because it can use packing, obfuscation, process manipulation and runtime evasion techniques. However, conventional multimodal usually uses all a…