Abstract
The widespread adoption of autonomy in vehicle subsystems has led to modern vehicles being highly connected to various external systems, to achieve robust and high efficiency driving performance. This trend has led to increased software and hardware complexity in distributed electronic control units (ECUs) inside vehicles, as well as an increase in ECU count. Modern automotive systems thus represent highly complex distributed cyber-physical IoT system. Unfortunately, the increased system complexity and interactions with external systems have made the automotive systems highly vulnerable to various cyberattacks. The growing compute power and the large availability of automotive network data make emerging artificial intelligence (AI)-based solutions a promising choice for improving cybersecurity in automotive systems. In this chapter, we provide an overview of novel AI-based intrusion detection system (IDS) framework called INDRA that actively monitors messages in the in-vehicle network to detect cyberattacks in automotive systems. INDRA utilizes a gated recurrent unit (GRU)-based recurrent autoencoder architecture which helped in achieving a superior IDS performance with low overhead (low memory footprint and faster detection times). The evaluation of INDRA under various attack scenarios, and comparison with the state-of-the-art prior works is presented at the end of the chapter.
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References
Kukkala, V., Tunnell, J., Pasricha, S.: Advanced driver assistance systems: a path toward autonomous vehicles. IEEE Consum. Electron. 7(5), 18–25 (2018)
Koscher, K., et al.: Experimental security analysis of a modern automobile. In: IEEE SP (2010)
Valasek, C., et al.: Remote exploitation of an unaltered passenger vehicle. https://ioactive.com/pdfs/IOActive_Remote_Car_Hacking.pdf (2015)
Izosimov, V., et al.: Security-aware development of cyber-physical systems illustrated with automotive case study. In: IEEE/ACM DATE (2016)
Studnia, I., et al.: A language-based intrusion detection approach for automotive embedded network. In: IEEE PRISDC (2015)
Marchetti, M., et al.: Anomaly detection of CAN bus messages through analysis of ID sequences. In: IEEE IV (2017)
Hoppe, T., et al.: Security threats to automotive CAN networks- practical examples and selected short-term countermeasures. In: RESS (2011)
Larson, U.E., et al.: An approach to specification-based attack detection for in-vehicle networks. In: IEEE IV (2008)
Aldwairi, M., et al.: Pattern matching of signature-based IDS using Myers algorithm under MapReduce framework. In: EURASIP (2017)
Myers, E.W.: An O(ND) difference algorithm and its variations. In: Algorithmica (1986)
Hoppe, T., et al.: Applying intrusion detection to automotive IT-early insights and remaining challenges. In: JIAS (2009)
Waszecki, P. et al.: Automotive electrical and electronic architecture security via distributed in-vehicle traffic monitoring. In: IEEE TCAD (2017)
Cho, K.T., et al.: Fingerprinting electronic control units for vehicle intrusion detection. In: USENIX (2016)
Ying, X., et al.: Shape of the Cloak: formal analysis of clock skew-based intrusion detection system in controller area networks. In: IEEE TIFS (2019)
Yoon, M.K., et al.: Memory heat map: anomaly detection in real-time embedded systems using memory behavior. In: IEEE/ACM/EDAC DAC (2015)
Müter, M., et al.: Entropy-based anomaly detection for in-vehicle networks. In: IEEE IV (2011)
Müter, M., et al.: A structured approach to anomaly detection for in-vehicle networks. In: ICIAS (2010)
Taylor, A., Japkowicz, N., Leblanc, S.: Frequency-based anomaly detection for the automotive CAN bus. In: Proceedings of WCICSS (2015)
Martinelli, F., et al.: Car hacking identification through fuzzy logic algorithms. In: FUZZ-IEEE (2017)
Vuong, T.P., et al.: Performance evaluation of cyber-physical intrusion detection on a robotic vehicle. In: IEEE CIT/IUCC/DASC/PICOM (2015)
Levi, M., Allouche, Y., et al.: Advanced analytics for connected cars cyber security [Online]. Available: https://arxiv.org/abs/1711.01939 (2017)
Kang, M.-J., et al.: A novel intrusion detection method using deep neural network for in-vehicle network security. In: IEEE, VTC Spring (2016)
Hanselmann, M., et al.: CANet: an unsupervised intrusion detection system for high dimensional CAN bus data: In: IEEE Access (2020)
Loukas, G., et al.: Cloud-based cyber-physical intrusion detection for vehicles using deep learning. In: IEEE Access (2018)
Taylor, A., et al.: Anomaly detection in automobile control network data with long short-term memory networks. In: IEEE DSAA (2016)
Weber, M., et al.: Online detection of anomalies in vehicle signals using replicator neural networks. In: ESCAR (2018)
Weber, M., et al.: Embedded hybrid anomaly detection for automotive can communication. In: Embedded Real Time Software and Systems (2018) [Online]. Available: https://hal.archives-ouvertes.fr/hal-01716805
Schmidhuber, J.: Habilitation thesis: system modeling and optimization (1993)
Hochreiter, S., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: IEEE Press (2001)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)
DiDomenico, G., et al.: Colorado State University EcoCAR 3 final technical report. In: SAE, WCX (2019)
Kukkala, V., Pasricha, S., Bradley, T.H.: SEDAN: Security-aware design of time-critical automotive networks. IEEE Trans. Veh. Technol. (TVT) 69(8), 9017–9030 (2020, August)
Kukkala, V.K., Thiruloga, S.V., Pasricha, S.: INDRA: intrusion detection using recurrent autoencoders in automotive embedded systems. IEEE Trans. Comput-Aid. Des. Integr. Circuits Syst. (TCAD) 39(11) (2020, November)
Thiruloga, S.V., Kukkala, V.K., Pasricha, S.: TENET: temporal CNN with attention for anomaly detection in automotive cyber-physical systems. In: IEEE/ACM Asia & South Pacific Design Automation Conference (ASPDAC) (2022, January)
Kukkala, V.K, Thiruloga, S.V., Pasricha, S.: LATTE: LSTM self-attention based anomaly detection in embedded automotive platforms. In: ACM Transactions on Embedded Computing Systems (TECS) (2021)
Kukkala, V.K., Thiruloga, S.V., Pasricha, S.: Roadmap for cybersecurity in autonomous vehicles. In: IEEE Consumer Electronics (2022)
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This research is supported by a grant from NSF (CNS-2132385).
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Kukkala, V.K., Thiruloga, S.V., Pasricha, S. (2023). AI for Cybersecurity in Distributed Automotive IoT Systems. In: Iranmanesh, A. (eds) Frontiers of Quality Electronic Design (QED). Springer, Cham. https://doi.org/10.1007/978-3-031-16344-9_8
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DOI: https://doi.org/10.1007/978-3-031-16344-9_8
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