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对物联网数据驱动攻击检测趋势的系统审查
Safwana Haque1, Fadi El-Moussa2, Nikos Komninos1
1Department of Electrical and Electronic Engineering, School of Science & Technology, City, University of London, Northampton Square, London EC1V 0HB, UK.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
物联网 (IoT) 面临着重大的安全挑战. 这项研究调查了用于物联网威胁检测的机器学习和深度学习,分析数据集和算法效率,以指导未来的研究.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 是日常生活的组成部分,提供方便,但会带来重大安全风险.
- 现有的网络安全措施往往无法解决物联网环境的独特漏洞.
- 快速发展的技术和攻击者的复杂性需要物联网的先进安全解决方案.
研究的目的:
- 调查物联网威胁检测中的当前安全趋势.
- 专注于机器学习 (ML) 和深度学习 (DL) 技术在物联网安全中的应用.
- 提供可用的物联网数据集和ML/DL算法的性能的全面概述.
主要方法:
- 对物联网安全方面的最新研究进行系统审查.
- 对机器学习和深度学习算法的分析,应用于物联网威胁检测.
- 在各种相关的物联网数据集中评估算法效率.
主要成果:
- 识别用于物联网威胁检测的流行的ML/DL技术.
- 在各种物联网数据集上评估不同算法的有效性.
- 编目可用的物联网数据集用于安全研究.
结论:
- 机器学习和深度学习在增强物联网安全方面表现有前途.
- 需要进一步的研究来优化算法并解决特定的物联网漏洞.
- 这项调查是物联网安全研究人员和从业人员的宝贵资源.
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