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

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...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 77; pp. 339 - 353
Main Authors: Liu, Liang, Yang, Jingxiu, Meng, Weizhi
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Ltd 01.07.2019
Elsevier BV
Subjects:
Algorithms
Design optimization
Gradient descent
Internet of Things
K-means
Machine learning
Malicious node detection
Nodes
Route planning
Support vector machine
Support vector machines
Trust management
K-means
Internet of things
Gradient descent
Machine learning
Malicious node detection
Support vector machine
Trust management
ISSN:0045-7906, 1879-0755
Online Access:Get full text
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Summary: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%.
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ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2019.06.013