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- W4386816097 abstract "The intrusion detection techniques remain essential for network security, especially for the Internet of Things (IoT) environment, where there are crucial network systems and voluminous intra-network devices with vulnerabilities and the need for protection. Despite the existence of many deep learning-based approaches for effective and efficient intrusion detection, this field still faces the difficulties of imbalanced classification and limited labelled samples. While it is impossible for supervised methods to bypass these challenges, self-supervised learning can characterize both benign and malicious behaviours while requiring no malicious samples for training. In this paper, we propose a self-supervised learning-based one-class classification (SLOSC) approach for detecting network attacks in IoT environment. This deep learning one-class classification model allows detecting attacks while being trained with only legitimate data. We conducted experiments on the public IoT botnet attack dataset, and the results showed that our method could outperform both traditional models like Gaussian mixture and one-class SVM and state-of-the-art unsupervised deep learning models." @default.
- W4386816097 created "2023-09-18" @default.
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- W4386816097 date "2023-01-01" @default.
- W4386816097 modified "2023-09-30" @default.
- W4386816097 title "Train Without Label: A Self-supervised One-Class Classification Approach for IoT Anomaly Detection" @default.
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- W4386816097 doi "https://doi.org/10.1007/978-3-031-43792-2_8" @default.
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