Three-layer hybrid intrusion detection model for smart home malicious attacks. (December 2021)

• Record Type: Journal Article
• Authors: Shi, Lingyun; Wu, Longfei; Guan, Zhitao
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Internet of things -- Smart home -- Intrusion detection system -- Machine learning -- Security; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107536
• Languages: English
• ISSNs: 0045-7906

Abstract

Research highlights: Attack detection of smart home based on Internet of things security architecture The two-layer data preprocessing method suitable for massive data scene of smart home Detection model composed of binary classifiers improving the accuracy of malicious behavior 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.