Analyzing the effectiveness of IDS/IPS in real-time with a custom in-vehicle design
Author:
Funder
University of Turku
Publisher
Elsevier BV
Reference19 articles.
1. Adu-Kyere, A., Nigussie, E., Isoaho, J., 2023. Self-aware cybersecurity architecture for autonomous vehicles: Security through system-level accountability. Sensors 23, 8817. URL: https://www.mdpi.com/1424-8220/23/21/8817, doi:10.3390/s23218817.
2. Intrusion detection systems for intra-vehicle networks: A review;Al-Jarrah;IEEE Access,2019
3. Desta, A.K., Ohira, S., Arai, I., Fujikawa, K., 2022. Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots. Vehicular Communications 35, 100470. URL: https://doi.org/10.1016/j.vehcom.2022.100470, doi:10.1016/j.vehcom.2022.100470.
4. Trusted execution environments: Properties, applications, and challenges;Jauernig;IEEE Security and Privacy,2020
5. CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU;Javed;IEEE Transactions on Network Science and Engineering,2021
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