Open Access

ARTICLE

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Bing Liu¹, Zhe Zhang¹, Shengrong Hu², Song Sun^3,*, Dapeng Liu⁴, Zhenyu Qiu⁵

1 Zhejiang Institute of Industry and Information Technology, Hangzhou, 310000, China
2 Digital Economy Development Center of Zhejiang, Hangzhou, 310000, China
3 College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China
4 Bank of Suzhou, Suzhou, 215000, China
5 Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, 310051, China

* Corresponding Author: Song Sun. Email:

(This article belongs to the Special Issue: Enhancing IoT Cyber-Resilience: Convergence of AI, Digital Twins, and the Metaverse )

Computers, Materials & Continua 2024, 78(3), 4049-4069. https://doi.org/10.32604/cmc.2024.048099

Received 27 November 2023; Accepted 29 January 2024; Issue published 26 March 2024

Abstract

Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device. In the presence of a high false positive rate in ADSs, to deal with the trade-off between the security and availability of data, we develop a linear programming model referred to as a security trade-off (ST) model. Next, we develop an analysis framework for the ST model, and solve the ST model on an IoT simulation platform. Last, we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis. Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

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Keywords

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