Visible to the public A Multilayer Perceptron-Based Distributed Intrusion Detection System for Internet of Vehicles

TitleA Multilayer Perceptron-Based Distributed Intrusion Detection System for Internet of Vehicles
Publication TypeConference Paper
Year of Publication2018
AuthorsAnzer, Ayesha, Elhadef, Mourad
Conference Name2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)
Date PublishedOct. 2018
ISBN Number978-1-5386-9502-9
KeywordsAccidents, Ad hoc networks, distributed intrusion detection system, distributed processing, Embedded systems, human factors, Internet of Vehicles, Internet of Vehicles (IoV), Internet of vehicles network, Intrusion detection, IoV network, Metrics, MLP, Multilayer Perceptron, MultiLayer Perceptron neural network, multilayer perceptrons, pubcrawl, resilience, Resiliency, Roads, security attacks, Sensors, telecommunication computing, telecommunication security, V2V communication, VANET, Vehicle to Vehicle communication, vehicular ad hoc networks, Wireless sensor networks

Security of Internet of vehicles (IoV) is critical as it promises to provide with safer and secure driving. IoV relies on VANETs which is based on V2V (Vehicle to Vehicle) communication. The vehicles are integrated with various sensors and embedded systems allowing them to gather data related to the situation on the road. The collected data can be information associated with a car accident, the congested highway ahead, parked car, etc. This information exchanged with other neighboring vehicles on the road to promote safe driving. IoV networks are vulnerable to various security attacks. The V2V communication comprises specific vulnerabilities which can be manipulated by attackers to compromise the whole network. In this paper, we concentrate on intrusion detection in IoV and propose a multilayer perceptron (MLP) neural network to detect intruders or attackers on an IoV network. Results are in the form of prediction, classification reports, and confusion matrix. A thorough simulation study demonstrates the effectiveness of the new MLP-based intrusion detection system.

Citation Keyanzer_multilayer_2018