Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Abstract

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.