Detecting malicious nodes via gradient descent and support vector machine in Internet of Things. (July 2019)

• Record Type: Journal Article
• Authors: Liu, Liang; Yang, Jingxiu; Meng, Weizhi
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 339
• Page End: 353
• Publication Date: 2019-07
• Subjects: Internet of things -- Malicious node detection -- Support vector machine -- Gradient descent -- K-means -- Trust management -- Machine learning; 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.2019.06.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: IoT devices have become much popular in our daily lives, while attackers often invade network nodes to launch various attacks. In this work, we focus on the detection of insider attacks in IoT networks. Most existing algorithms calculate the reputation of all nodes based on the routing path. However, they rely heavily on the assumption that different nodes in the same routing path have equal reputation, which may be not invalid in practice and cause inaccurate detection results. To solve this issue, we formulate it as a multivariate multiple linear regression problem and use the K -means classification algorithm to detect malicious nodes. Further, we optimize the routing path and design an enhanced detection scheme. Our results indicate that our proposed methods could achieve a detection accuracy rate of 90% or above in a common case, and the enhanced scheme could reach an even lower false detection rate, i.e., below 5%.