Replica attack detection method for vehicular ad hoc networks with sequential trajectory segment

Yan Xin and Xia Feng

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 2, 1550147719827500

Abstract: In vehicular ad hoc networks, attackers can disguise as replicas of legitimate vehicles by cracking or colluding and then use the identity replicas in a malicious way. Not only the generation of replicas itself poses an aggressive behavior, but also the replicas can enable other insider attacks, such as denial of service, information interception, and replay attack. To solve this issue, researchers have presented many solutions in wireless sensor network or in mobile ad hoc networks. However, majority of current schemes are not good at dealing with conspiracy replicas or lack of considering peculiar characteristics of high mobility of vehicles. For detecting identity replicas in vehicular ad hoc networks, we propose a detection method with sequential trajectory segment based on semi-supervised support vector machine. In terms of semi-supervised support vector machine, we establish a detection model using spatio-temporal trajectories of different identities as input samples, which include features of both conspiracy and non-conspiracy attack scenarios. To validate our approach, we apply sequential trajectory segment to simulation environment. The performance analysis and experimental studies suggest that our proposed method provides high detection accuracy, which is almost impervious to the replica identity ratios in vehicular ad hoc networks. Furthermore, the time performance of replica detection is less affected by the distance between compromised nodes and their clones than that of existing solutions.

Keywords: Vehicular ad hoc networks; identity replicas; spatio-temporal trajectory; semi-supervised support vector machine; replica attack detection (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:2:p:1550147719827500

DOI: 10.1177/1550147719827500

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