Intrusion-Based Attack Detection Using Machine Learning Techniques for Connected Autonomous Vehicle

With advancements in technology, an important issue is ensuring the security of self-driving cars. Unfortunately, hackers have been developing increasingly complex and harmful cyberattacks, making them difficult to detect. Furthermore, due to the diversity of the data exchanged amongst these vehicles, traditional algorithms face difficulty detecting such threats. Therefore, a network intrusion detection system is essential in a connected autonomous vehicle's communication infrastructure. The IDS (intrusion detection system) aims to secure the network by identifying malicious and abnormal traffic in real-time. This paper focuses on the data preprocessing, feature extraction, attack detection for such a system. Additionally, it will compare the performance of this proposed IDS when operating in different machine learning models. We apply Linear Regression (LR), Linear Discriminant Analysis (LDA), K Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Support Vector Machine (SVM) to classify the NSL-KDD dataset. The dataset was classified using binary and multiclass classification to train and test files. This data resulted in 94% and 98% accuracy for the train and test files, respectively, with KNN and CART algorithms.