A hybrid artificial intelligence framework for enhancing digital forensic investigations of infotainment systems

Abstract

Infotainment systems in vehicles have become important sources of digital evidence in forensic investigations. Analyzing data from these systems can provide valuable insights into a suspect's activities and interactions. In this paper, we propose a hybrid artificial intelligence (AI) framework that combines unsupervised learning using K-means clustering and language model analysis to enhance the forensic analysis process. The proposed methodology was applied to two distinct datasets from Hyundai and Mitsubishi infotainment systems. In the Hyundai dataset, the recall for contact names and phone numbers improved by 18% and 3% respectively when compared to clustering alone. Similarly, in the Mitsubishi dataset, the recall of song names improved by 2%. In addition, this hybrid approach enabled the discovery of more forms of forensically-relevant data stored in the infotainment systems, such as geographical locations and connected devices, that would have been infeasible to find with either manual analysis or clustering alone. Despite the presence of some hallucinations, the combination of these techniques resulted in improved ease of analysis and increased recall, demonstrating the potential of this hybrid approach in forensic investigations.