Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.

The current study emphasizes on improving an interval type-2 fuzzy logic control (IT2FLC) system through the use of spiral dynamics algorithm (SDA) optimization in stabilizing a transformational two-wheeled wheelchair. The main contribution of this research is to reduce vibrations while performing the lifting and stabilization of a wheelchair from its standard four-wheeled to two-wheeled transformation. IT2FLC based SDA was used to enhance the system’s stability performance by obtaining the optimized value for input and output controller gains and IT2FLC parameters for IT2FLC. System modeling was done through development within the SimWise 4D software environment, which was then integrated with MATLAB/SIMULINK for control purposes. The proposed algorithm has demonstrated improved tilt angle performance with reduced noise and lower torque when various disturbances were applied, as compared to a system solely controlled by IT2FLC without any optimization. Moreover, the proposed algorithm has also comprehensively outperformed previous controllers in terms of system’s stability, further demonstrated its superiority as a system controller within transformational wheelchairs.