Vol. 1 No. 1 (2021): Journal of AI-Assisted Scientific Discovery
Articles

Deep Reinforcement Learning for Adaptive Cyber Defense in IoT-connected Autonomous Vehicle Networks

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  • Dr. Olga Volkova
Dr. Olga Volkova
Professor of Artificial Intelligence, National Research University – Information Technologies, Mechanics and Optics (ITMO)
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Published 30-06-2021

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How to Cite

[1]
Dr. Olga Volkova, “Deep Reinforcement Learning for Adaptive Cyber Defense in IoT-connected Autonomous Vehicle Networks”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 30–49, Jun. 2021, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/62

Abstract

In particular, the novel cyber defence mechanism is based on a robust, Reinforcement Learning-based Intrusion Detection & Response (RLIDR) module, which receives encrypted Low-Level Telemetry & Control (LLT&C) data streams and has an interface with a Human-Machine Interface (HMI). The architecture is based on Long Short-Term Memory (LSTM) recurrent neural network and convolutional layers, useful to understand waveforms and other hidden and non-linear information. LSTM is useful in the detection mechanisms because it can process the incoming LLT&C data stream, monitoring latency, signal bitrate and deep messages sharing with respect to the classical sequence-to-sequence Terminator-Transformer model. Attack resistance properties are developed over time by having an adaptation phase to the network characteristics, and this means that a proper Reinforcement Learning (RL) algorithm was conceived and selected to adapt dynamic energy driven threat against the system, considering the different behavior of the threat found in time with respect to the vehicle dynamics. Finally, a simulation in a controlled IoT scenario is shown, based on real telecommunication data from the metropolitan area of the city of Milano, to give evidence that the proposed RLIDR is quite robust.

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