ODPV: An Efficient Protocol to Mitigate Data Integrity Attacks in Intelligent Transport Systems

• Published in: IEEE access Vol. 8; pp. 114733 - 114740
• Main Authors: Javed, Muhammad Awais, Khan, Mohammad Zubair, Zafar, Usman, Siddiqui, Muhammad Faisal, Badar, Rabiah, Lee, Byung Moo, Ahmad, Farhan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomaly detection; Data analysis; Data integrity; Decision making; Forestry; Fuzzy logic; Integrity; intelligent transport systems; Intelligent transportation systems; Nodes; Outliers (statistics); Protocols; Roads; Traffic management; Vegetation; vehicular network
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3004444

Intelligent Transport Systems (ITS) require accurate information to be shared among vehicles and infrastructure nodes for applications including accident information or pre-crash warnings, to name a few. Due to its sensitive nature, ITS applications are vulnerable against data integrity attacks where nodes transmit false information that results in wrong decision making by the applications. A characteristic of such attacks is that the false transmitted information is significantly different than the actual information. In this paper, we propose an Outlier Detection, Prioritization and Verification (ODPV) protocol that efficiently isolates false data and improves traffic management decisions. ODPV uses the isolation forest algorithm to detect outliers, fuzzy logic to prioritize outliers and C-V2X communications to verify the outliers. Extensive simulation results verify the effectiveness of the proposed protocol to isolate the outliers.