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• W3208346170 abstract "With the development of Internet of Things and the increasingly rampant malicious network activities, higher requirements are put forward for security to detect malicious behavior and prevent attackers from obtaining sensitive data in the smart home environment. In this paper, an intrusion detection system is proposed to detect and classify abnormal behavior in the smart home environment. The two-layer feature processing method based on random forest and principal component analysis can reduce the loss of data information and is suitable for massive data. The three-layer detection model can detect four common attacks with binary classifiers and effectively improve the accuracy. The experimental evaluation of the proposed model is conducted using the real smart home traffic dataset and achieves a classification accuracy of 95.90%. The experimental results show that our model has a good performance in detecting and classifying malicious attacks in the smart home." @default.
• W3208346170 created "2021-11-08" @default.
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• W3208346170 date "2021-12-01" @default.
• W3208346170 modified "2023-10-17" @default.
• W3208346170 title "Three-layer hybrid intrusion detection model for smart home malicious attacks" @default.
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• W3208346170 doi "https://doi.org/10.1016/j.compeleceng.2021.107536" @default.
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