Machine Learning for Autonomous Vehicle Network Intrusion Detection

According to this study, autonomous vehicles (AV) employ a network IDS or Intrusion Detection System with tree-structured machine learning model. Techniques such as XGBoost, additional trees, decision trees and random forests are utilised in ML. A well-known classification method that uses a divide-and-conquer approach is the DT or Decision Tree. Each leaf node and decision node in a DT represents a criterion test for a particular attribute and outcome class. The decision tree class with the most votes, according to the majority voting rule, decides 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 derived through the analysis of a large volume of dataset. 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 combination of feature selection and ensemble learning in the suggested technique results in high detection rates at low processing costs. 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.