Abstract
Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this chapter, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks (CNNs) with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post-deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET achieves an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, 86.95% decrease in memory footprint, and 48.14% lower inference time when compared to the best performing prior work on automotive anomaly detection.
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References
Kukkala, V.K., Tunnell, J., Pasricha, S., Bradley, T.: Advanced driver-assistance systems: a path toward autonomous vehicles. In: Proceeding of IEEE CEM (2018)
Kukkala, V.K., Pasricha, S., Bradley, T.: SEDAN: security-aware design of time-critical automotive networks. In IEEE TVT (2020)
Valasek, C., Miller, C.: Remote exploitation of an unaltered passenger vehicle. In BlackHat USA (2015)
Adventures in Automotive Networks and Control Units, [online]: https://ioactive.com/pdfs/IOActive_Adventures_in_Automotive_Networks_and_Control_Units.pdf (2013)
Sitawarin, C., Bhagoji, A.N., Mosenia, A., Chiang, M., Mittal, P.: DARTS: deceiving autonomous cars with toxic signs. In: ArXiv (2018)
Dürrwang, J., Braun, J., Rumez, M., Kriesten, R., Pretschner, A.: Enhancement of Automotive Penetration Testing with Threat Analyses Results. In: SAE (2018)
Song, H.M., Kim, H.R., Kim, H.K.: Intrusion detection system based on the analysis of time intervals of can messages for in-vehicle network. In: ICOIN (2016)
Gmiden, M., Gmiden, M.H., Trabelsi, H.: An intrusion detection method for securing in-vehicle CAN bus. In: IEEE Proceeding of STA (2016)
Lee, H., Jeong, S.H., Kim, H.K.: OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In: PST (2017)
Marchetti, M., Stabili, D.: Anomaly detection of CAN bus messages through analysis of ID equences. In: IEEE IV (2017)
Müter, M., Asaj, N.: Entropy-based anomaly detection for in-vehicle networks. In: IV (2011)
Wu, W., Huang, Y., Kurachi, R., Zeng, G., Xie, G., Li, R., Li, K.: Sliding window optimized information entropy analysis method for intrusion detection on in-vehicle networks. In: IEEE Access (2018)
Stabili, D., Marchetti, M., Colajanni, M., Detecting attacks to internal vehicle networks through hamming distance. In: AEIT (2017)
Shin, K.G., Shin, K.G.: Viden: attacker identification on in-vehicle networks. In: ACM SIGSAC (2017)
Kang, M., Kang, J.: A novel intrusion detection method using deep neural network for in-vehicle network security. In: VTC (2016)
Weber, M., Wolf, G., Zimmer, B., Sax, E.: Online detection of anomalies in vehicle signals using replicator neural networks. In: ESCAR (2018)
Hanselmann, M., Strauss, T., Dormann, K., Ulmer, H.: CANet: an unsupervised intrusion detection system for high dimensional can bus data. In: IEEE Access (2020)
Loukas, G., Vuong, T., Heartfield, R., Sakellari, G., Yoon, Y., Gan, D.: Cloud-based cyber-physical intrusion detection for vehicles using deep learning. In: IEEE Access (2018)
Hossain, M.A., Inoue, H., Ochiai, H., Fall, D., Kadobayashi, Y. LSTM-based intrusion detection system for in-vehicle can bus communications. In: IEEE Access (2020)
Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in automobile control network data with long short-term memory networks. In: DSAA (2016)
Kukkala, V.K., Thiruloga, S.V., Pasricha, S.: INDRA: intrusion detection using recurrent autoencoders in automotive embedded systems. In: TCAD (2020)
Ezeme, M., Mahmoud, Q.H., Azim, A.: Hierarchical attention-based anomaly detection model for embedded operating systems. In: RTCSA (2018)
Tariq, S., Lee, S., Woo, S.S.: CANTransfer: Transfer learning based intrusion detection on a controller area network using convolutional lstm network. In: ACM SAC (2020)
Kukkala, V.K., Thiruloga, S.V., Pasricha, S.: LATTE: LSTM self-attention based anomaly detection in embedded automotive platforms. In: ACM TECS (2021)
[online] https://assets.vector.com/cms/content/products/gl_logger/Docs/GL1000_Manual_ EN.pdf
Waibel, T., Hanazawa, G., Hinton, K.S., Lang, K.J.: Phoneme recognition using timedelay neural networks. In: IEEE Transactions on Acoustics, Speech, and Signal Processing (1989)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. In: ArXiv (2018)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: ArXiv (2017)
Thiruloga, S.V., Kukkala, V.K., Pasricha, S.: TENET: temporal cnn with attention for anomaly detection in automotive cyber-physical systems. In: IEEE/ACM ASPDAC (2022)
Kukkala, V.K., Bradley, T., Pasricha, S.: Priority-based multi-level monitoring of signal integrity in a distributed powertrain control system. In: Proceeding of IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (2015)
Kukkala, V.K., Bradley, T., Pasricha, S.: Uncertainty Analysis and Propagation for an Auxiliary Power Module. In: Proceeding of IEEE Transportation Electrification Conference (TEC) (2017)
Kukkala, V.K., Pasricha, S., Bradley, T.: JAMS: Jitter-Aware Message Scheduling for FlexRay Automotive Networks. In: Proceeding of IEEE/ACM International Symposium on Network-on-Chip (NOCS) (2017)
Kukkala, V.K., Pasricha, S., Bradley, T.: JAMS-SG: a framework for jitter-aware message scheduling for time-triggered automotive networks. In: ACM Transactions on Design Automation of Electronic Systems (TODAES), Vol. 24, No. 6 (2019)
Kukkala, V.K., Thiruloga, S.V., Pasricha, S.: Roadmap for cybersecurity in autonomous vehicles. In: IEEE Consumer Electronics Magazine (CEM) (2022)
Tunnell, J., Asher, Z., Pasricha, S., Bradley, T.H.: Towards improving vehicle fuel economy with ADAS. In: SAE Inter-national Journal of Connected and Automated Vehicles, Vol. 1, No. 2 (2018)
Tunnell, J., Asher, Z., Pasricha, S., Bradley, T.H.: Towards Improving Vehicle Fuel Economy with ADAS. In Proceeding of SAE World Congress Experience (WCX) (2018)
Asher, Z., Tunnell, J., Baker, D.A., Fitzgerald, R.J., Banaei-Kashani, F., Pasricha, S., Bradley, T.H.: Enabling prediction for optimal fuel economy vehicle control. In Proceeding of SAE World Congress Experience (WCX) (2018)
Dey, J., Taylor, W., Pasricha, S.: VESPA: a framework for optimizing heterogeneous sensor placement and orientation for autonomous vehicles. In IEEE Consumer Electronics Magazine (CEM), Vol. 10, No. 2 (2021)
DiDomenico, G.C., Bair, J., Kukkala, V.K., Tunnell, J., Peyfuss, M., Kraus, M., Ax, J., Lazarri, J., Munin, M., Cooke, C., Christensen, E.: Colorado state university EcoCAR 3 final technical report. In: SAE World Congress Experience (WCX) (2019)
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Thiruloga, S.V., Kukkala, V.K., Pasricha, S. (2023). Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems. In: Kukkala, V.K., Pasricha, S. (eds) Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-28016-0_12
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DOI: https://doi.org/10.1007/978-3-031-28016-0_12
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