Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143596
Title: A filter model for intrusion detection system in Vehicle Ad Hoc Networks : a hidden Markov methodology
Authors: Liang, Junwei
Ma, Maode
Muhammad Sadiq
Yeung, Kai-Hau
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Liang, J., Ma, M., Muhammad Sadiq, & Yeung, K.-H. (2019). A filter model for intrusion detection system in Vehicle Ad Hoc Networks : a hidden Markov methodology. Knowledge-Based Systems, 163, 611–623. doi:10.1016/j.knosys.2018.09.022
Journal: Knowledge-Based Systems 
Abstract: Although Vehicle Ad Hoc Network (VANETs) as a new technology is being used in wide range of applications to improve the driving experience as well as safety, it is vulnerable to various type of network attacks. Literature studies have revealed several reliable approaches based on intrusion detection system (IDS), to protect VANETs against attacks. However, by those solutions, the overheads of IDSs are serious which cause too long detection time, especially when the number of vehicles increases. In this paper, we propose a novel filter model based hidden Markov model (HMM) (FM-HMM) for IDS to reduce the overhead and time for detection without impairing detection rate. To the best of our knowledge, this is the first work in the literature to model the state pattern of each vehicle in VANETs as a HMM to quickly filter the messages from the vehicles instead of detecting these messages. The FM-HMM consists of three modules, i.e., schedule, filter and update. In the schedule module, Baum–Welch algorithm is used to produce a HMM and its parameters for each neighbor vehicle. In the filter module, multiple HMMs are used with their parameters to forecast the future states of neighbor vehicles with which the messages from them are filtered. In the update module, a timeliness method is used to update HMMs and their parameters. Experiments show that the IDS with FM-HMM has a better performance in terms of detection rate, detection time and overhead.
URI: https://hdl.handle.net/10356/143596
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2018.09.022
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Knowledge-Based Systems and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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