Enhancing Intrusion Detection in Autonomous Vehicles Using Tree-Based Machine Learning Techniques

According to this study, autonomous vehicles (AV) employ a network intrusion detection system with a tree-structured machine learning model (IDS). In machine learning techniques such as XGBoost, additional trees, random forests, and decision trees, trees are utilized. The decision tree is a well-known classification technique that employs a divide-and-conquer strategy (DT). Each leaf node and decision node in a decision tree represents a criterion test for a particular attribute and outcome class (DT). Using the majority voting rule, the decision tree class with the most votes determine the categorization outcome. This is how Random Forest, an ensemble learning-based classification algorithm, works (RF). Extra Trees (ET), a comparable ensemble model, is made up of a large number of randomly generated decision trees generated by analyzing a large number of data samples. To improve performance and speed, the XGBoost ensemble learning system employs gradient descent and multiple decision trees. The IDS multi classification problem is solved using machine learning algorithms. The proposed method achieves high detection rates at low processing costs by combining feature selection and ensemble learning. The computational efficiency of well-known supervised machine learning techniques is also considered when selecting a model. The proposed method for detecting different threats in anti-virus (AV) networks produced promising results.