AI-Powered Behavioral Analysis of Vehicle Communication to Strengthen API Security

Abstract

As vehicles become increasingly connected, the volume of API communication between cars and cloud-based services grows, exposing new security risks. Traditional rule-based security systems, such as AWS Web Application Firewall, are limited to detecting known threats and patterns that can be pre-defined in the ruleset. This thesis explores the use of AI-powered anomaly detection, specifically the Isolation Forest algorithm, as a complement to existing rule-based methods to secure API traffic in connected vehicles. A series of experiments were conducted using both synthetic and real-world API request data. The results show that Isolation Forest can effectively detect anomalous requests, especially when trained on sufficiently large and representative datasets. Comparisons with a rule-based system revealed that AI-based methods might be better at identifying unknown threats, while rulebased filters remain reliable for known attack patterns. Overall, the study highlights the potential of combining machine learning with traditional approaches to create more adaptive and intelligent API security systems for connected vehicles.