Anomaly Detection for Autonomous Vehicle Systems
In this project, I spearheaded a research initiative focused on developing advanced anomaly detection models for in-vehicle networks of autonomous vehicles at The Hong Kong Polytechnic University. The primary objective was to enhance the security and reliability of these networks by identifying and addressing potential vulnerabilities and threats. The project entailed the meticulous collection and annotation of vast datasets from vehicle network communications to train and validate three distinct detection models. Each model was designed to autonomously monitor critical vehicle functions such as braking, steering, and throttle systems. A significant portion of my role involved preprocessing the data to ensure its suitability for machine learning applications, which included cleaning, labeling, and segmenting the data based on operational parameters and observed anomalies. The success of the project was marked by the effective implementation of these models, which demonstrated high ac