机器学习和深度学习 物联网的技术 网络异常检测-当前研究趋势
Saida Hafsa Rafique1, Amira Abdallah1, Nura Shifa Musa1,2
1College of Information Technology, United Arab Emirates University, Abu Dhabi P.O. Box 15551, United Arab Emirates.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
物联网 (IoT) 中的异常检测得到了人工智能 (AI) 的增强. 本综述探讨了用于检测物联网网络中的威胁的机器学习和深度学习,强调了需要改进系统的需要.
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) 正在迅速扩展,产生大量数据并增加连接性.
- 异常检测对于识别物联网环境中系统正常行为的偏差至关重要.
- 人工智能 (AI) 的整合显著提高了物联网系统中异常检测的有效性.
研究的目的:
- 审查有关物联网基础设施异常检测的现有文献.
- 探索机器学习和深度学习技术在物联网中用于异常检测的应用.
- 确定物联网异常检测方面的挑战和未来研究方向.
主要方法:
- 对物联网网络中异常检测方案的文献综述.
- 分析用于入侵检测的机器学习和深度学习方法.
- 总结了该领域最近的进展和挑战.
主要成果:
- 人工智能驱动的异常检测可以有效地识别物联网中的各种威胁,例如DDoS攻击和SQL注入.
- 智能入侵检测系统 (IDS) 对于保护具有大型攻击表面的物联网设备至关重要.
- 该研究对许多基于机器学习和深度学习的异常检测方法进行了分类和审查.
结论:
- 需要进一步开发以增强现有的物联网异常检测系统.
- 建议包括利用多样化的数据集,进行实时测试,并确保系统可扩展性.
- 持续的研究对于解决物联网网络威胁不断变化的局面至关重要.
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