Detecting malicious nodes via gradient descent and support vector machine in Internet of Things

Liang Liu, Jingxiu Yang, Weizhi Meng*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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%.

Original languageEnglish
JournalComputers & Electrical Engineering
Pages (from-to)339-353
Publication statusPublished - 1 Jul 2019


  • Gradient descent
  • Internet of things
  • K-means
  • Machine learning
  • Malicious node detection
  • Support vector machine
  • Trust management


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