HybridSecNet: In-Vehicle Security on Controller Area Networks Through a Hybrid Two-Step LSTM-CNN Model

Abstract

The modern Intelligent Vehicle (IV) is a complex technological marvel that heavily relies on the Controller Area Network (CAN) bus system to enable seamless communication among different electronic control units (ECUs). However, the CAN bus system lacks security mechanisms for authentication and authorization, leaving it vulnerable to various attacks. Malicious actors can freely broadcast CAN messages without protection, making the system susceptible to DoS, Fuzzing, and Spoofing attacks. Therefore, it is crucial to devise methods to safeguard modern vehicles from such threats. In this research paper, we introduce HybridSecNet, A hybrid two-step LSTM-CNN Model for Intrusion Detection, a deep learning-based architecture specifically designed to bolster in-vehicle security on Controller Area Networks (CAN). HybridSecNet comprises two stages of classification: the first stage employs long short-term memory (LSTM) to categorize input data as either normal or attacked, and the second stage further classifies the attacks into specific types using Convolutional Neural Networks (CNN). This two-step approach significantly enhances classification accuracy and reliability, yielding remarkable results with accuracy, precision, recall, and an F1-score of approximately 99.5% for CAN bus network attacks. Comparative analyses with existing single-step models underscore the superiority of our proposed model, demonstrating its potential to revolutionize in-vehicle security in the realm of modern intelligent vehicles.