面向物联网的入侵检测技术研究新进展
New Research Progress on Intrusion Detection Techniques for the Internet of Things
冯光升 1蒋舜鹏 1胡先浪 1马明宇1
作者信息
- 1. 哈尔滨工程大学计算机科学与技术学院,哈尔滨 150000
- 折叠
摘要
相较于传统入侵检测机制,智能化的入侵检测技术能够充分提取数据特征,具有更高的检测效率,但对数据样本标签的要求也更高.文章按数据样本标签从有监督和无监督角度对物联网入侵检测技术的最新进展进行综述.首先概述了基于签名的入侵检测方法,并基于有监督和无监督的分类分析了近期基于传统机器学习的入侵检测方法;然后分析了近期基于深度学习的入侵检测方法,分别对基于有监督、无监督、生成对抗网络和深度强化学习的入侵检测方法进行分析;最后分析总结了物联网入侵检测技术的研究挑战和未来的研究趋势.
Abstract
Compared to traditional intrusion detection mechanisms,the intelligent intrusion detection technology can fully extract data features,demonstrating higher detection efficiency,however,it also imposes greater demands on data sample labels.Considering data sample labels,this article provided a comprehensive review of the latest developments in the intrusion detection technology for the Internet of things(IoT)from the perspectives of supervised and unsupervised learning.Firstly,it outlined signature-based intrusion detection methods and analyzed recent traditional machine learning based intrusion detection methods based on the classification of supervised and unsupervised learning.Then,it analyzed recent deep learning based intrusion detection methods based on supervised,unsupervised,generative adversarial network,and deep reinforcement learning,respectively.Finally,it summarized the research challenges and future trends in the IoT intrusion detection technology.
关键词
物联网/入侵检测/机器学习/深度学习/生成对抗网络Key words
Internet of things/intrusion detection/machine learning/deep learning/generative adversarial network引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据