可解释的人工智能用于物联网中的尸网络检测
Mohamed Saied1, Shawkat Guirguis2
1Institute of Graduate Studies & Research, Alexandria University, 832, Elhorrya Road, Alexandria, 21526, Egypt. igsr.msaied@alexu.edu.eg.
Scientific reports
|March 4, 2025
概括
可解释的人工智能 (XAI) 通过提高模型透明度和可信度来增强物联网 (IoT) 尸网络检测. 这项研究证明了XAI.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网设备的普及增加了连接性,但也带来了重大安全挑战,特别是尸网络攻击.
- 由于设备多样性和大量数据量,在物联网环境中检测尸网络是很困难的.
- 人工智能和机器学习对物联网尸网络检测有希望,但缺乏决策透明度.
研究的目的:
- 提出和分析可解释的人工智能 (XAI) 技术的利用,以提高物联网尸网络检测的可解释性和透明度.
- 调查XAI对模型可信度和新出现的尸网络模式的早期检测的影响.
- 为保护物联网生态系统免受尸网络威胁提供实际指导.
主要方法:
- 将可解释的人工智能 (XAI) 技术纳入尸网络检测模型.
- 对三种XAI方法的分析:规则提取和蒸,局部可解释的模型不可知解释 (LIME) 和沙普利添加式解释 (SHAP).
- 对拟议的基于XAI的方法进行实验性评估.
主要成果:
- 实验结果证明了XAI在提高尸网络检测可解释性和透明度方面的有效性.
- XAI技术为检测模型的内部运作提供了宝贵的见解.
- 这种方法促进了对物联网尸网络攻击的强有力的防御机制的开发.
结论:
- XAI显著提高了基于AI/ML的物联网尸网络检测的可靠性和透明度.
- 该研究为XAI在网络安全研究中的贡献,并为保护物联网环境提供了实际见解.
- XAI 能够早期检测出新的尸网络攻击模式.
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