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Title: ECF-MRS : an efficient and collaborative framework with Markov-based reputation scheme for IDSs in vehicular networks
Authors: Liang, Junwei
Ma, Maode
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Liang, J. & Ma, M. (2020). ECF-MRS : an efficient and collaborative framework with Markov-based reputation scheme for IDSs in vehicular networks. IEEE Transactions On Information Forensics and Security, 16, 278-290.
Project: A19D6a0053
Journal: IEEE Transactions on Information Forensics and Security
Abstract: Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this article, an Efficient and Collaborative Framework with a Markov-based Reputation Scheme is proposed, namely ECF-MRS. In the proposed framework, the collaborative mechanism is achieved by using Non-dominant Sorting Genetic Algorithm-III (NSGA-III)-Collaboration to merge the advantages of IDSs in VANETs to generate a more superior IDS, while Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of collaboration, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the IDS with ECF-MRS has better performance than other existing IDSs in terms of detection rate, detection time and overhead.
ISSN: 1556-6013
DOI: 10.1109/TIFS.2020.3013211
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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