传感技术学报2024，Vol.37Issue(2)：317-325,9.DOI:10.3969/j.issn.1004-1699.2024.02.017
一种物联网入侵检测和成员推理攻击研究
A New Study of an IoT Intrusion Detection and Membership Inference Attack
摘要
Abstract
To adapt to the weak computing power of the Internet of Things nodes,insufficient storage space and the vulnerability of sensi-tive data attacks,a new lightweight intrusion detection model incorporating a convolutional neural network and differential privacy are proposed to make the model better adapted to the demanding resource environment of IoT.Firstly,the raw traffic data is pre-processed using the MinMax algorithm for normalization.Secondly,a lightweight convolutional neural network is designed to extract traffic features and perform classification.Finally,the differential privacy algorithm is used to defend against membership inference attacks that the model may encounter.The new algorithm is experimented on intrusion detection datasets such as UNSW_NB15,and the model accuracy reachs 98.98％,the precision rate reachs 98.05％,and the model size is controlled at about 200 KB,which improvs the accuracy rate by 2.81％compared with the DAE-OCSVM algorithm,and is suitable for the high accuracy intrusion detection required in the harsh envi-ronment of IoT.Meanwhile,the membership inference attack that the model may encounter are investigated,and the new algorithm re-duces 20.96％of the membership inference attack after incorporating the differential privacy algorithm.关键词
入侵检测/物联网/卷积神经网络/差分隐私/成员推理攻击Key words
intrusion detection system/internet of things/convolutional neural network/differential privacy/membership inference attack分类
信息技术与安全科学引用本文复制引用
项睿涵,潘巨龙,李玲艺,方堃..一种物联网入侵检测和成员推理攻击研究[J].传感技术学报,2024,37(2):317-325,9.基金项目
浙江省基础公益研究计划项目(LGF21F020017) （LGF21F020017）
