Swarm Learning and Knowledge Distillation Empowered Self-Driving Detection against Threat Behavior for Intelligent IoT

Document Type

Article

Publication Title

IEEE Transactions on Mobile Computing

Abstract

The combination of mobile communication and the Internet of Things (IoT) has made physical devices more intelligent, bringing great convenience to our lives. However, the deep integration of personal information and the Internet increases the risk of data leakage and is easily exploited maliciously. In addition, due to limited system resources, smart devices with lightweight design are required. Therefore, it is necessary to realize low-energy and effective abnormal behavior detection of IoT devices, but the existing detection methods have disadvantages such as leakage of user privacy, low accuracy, and difficulty in dynamically improving the effect. To address these issues, this paper proposes a dynamic interactive minor anomaly detection scheme called ADONIS based on Swarm Learning (SL). The scheme combines the concept of swarm defense and utilizes SL to achieve local data fusion, which improves the detection effect and protects user privacy. Moreover, the decentralized structure of SL can cope with the impact of single node damage to enhance the robustness of IoT services. Furthermore, we propose training and detection decoupling framework to achieve high accuracy, low energy consumption, and low latency. It improves the performance by fitting the training model with full data, and simplifies the complexity of the detection model using knowledge distillation. We also design a self-enhancing dynamic strategy based on the decoupling framework to maintain powerful detection capability through human-computer interaction (HCI) and continuous learning. The framework relies on traffic data to keep the model sensitive to new behavior through iterative training without disturbing the user. Finally, simulation experiments show that our proposed scheme can achieve 82.2% accuracy, reduce the average detection time to 8.22 $ ms$, and simplify the model complexity by 15.9%. Compared with existing methods, ADONIS can provide lighter, safer and more accurate anomaly detection.

First Page

7117

Last Page

7134

DOI

10.1109/TMC.2023.3330514

Publication Date

11-6-2023

Keywords

Internet of Things, Behavioral sciences, Training, Performance evaluation, Data models, Anomaly detection, Mobile computing

Comments

IR conditions: non-described

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