An intelligent active probing and trace-back scheme for IoT anomaly detection

Luying Wang ¹Lingyi Chen ¹Neal N.Xiong ²Anfeng Liu ¹Tian Wang ³Mianxiong Dong⁴

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作者信息

1. School of Computer Science and Engineering,Central South University,Changsha 410083,China
2. Department of Computer Science and Mathematics,Sul Ross State University,Alpine,TX 79830,USA
3. Artificial Intelligence and Future Networks,Beijing Normal University & UIC,Zhuhai 519087,China
4. Department of Information and Electronic Engineering,Muroran Institute of Technology,Muroran 050-8585,Japan
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Abstract

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33％respectively,while the accuracy of trust identification is improved by 20％.

Key words

Anomaly detection/Internet of things/Integrating data collection/Mobile edge users/Intelligent

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基金项目

国家自然科学基金(62072475)

中央高校基本科研业务费专项中南大学项目(CX20230356)

中南大学项目()

出版年

2024

数字通信与网络(英文)

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参考文献量34

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