SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

Abstract

With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking

“…With the development of AI technology, the robots have become a hot research spot in recent years [44][45][46][47][48]. To further validate the effectiveness and robustness of the ANPVZNN on real-time applications, comparative simulation results of the ANPVZNN and SBPAFactivated OZNN for robotic manipulator trajectory tracking with periodic noise is applied this section.…”