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An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks
journal contribution
posted on 2023-11-24, 04:13 authored by A Oseni, N Moustafa, G Creech, N Sohrabi, A Strelzoff, Z Tari, I LinkovThe security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system's effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks' security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15% accuracy and a 98.83% F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.
History
Journal
IEEE Transactions on Intelligent Transportation SystemsVolume
24Pagination
1000-1014Location
Piscataway, N.J.Publisher DOI
ISSN
1524-9050eISSN
1558-0016Language
EnglishPublication classification
C1.1 Refereed article in a scholarly journalIssue
1Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCUsage metrics
Categories
Keywords
Science & TechnologyTechnologyEngineering, CivilEngineering, Electrical & ElectronicTransportation Science & TechnologyEngineeringTransportationInternet of ThingsSecurityIntrusion detectionProtocolsDeep learningComputer architectureSafetyExplainable AInetwork intrusion detectiondeep learningIoTsecurityInternet of Vehicles (IoV)
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