Towards a unified XAI-based framework for digital forensic investigations

Abstract

Explainable Artificial Intelligence (XAI) aims to alleviate the black-box AI conundrum in the field of Digital Forensics (DF) (and others) by providing layman-interpretable explanations to predictions made by AI models. It also handles the increasing volumes of forensic images that are impossible to investigate via manual methods; or even automated forensic tools. A holistic, generalized, yet exhaustive framework detailing the workflow of XAI for DF is proposed for standardization. A case study examining the implementation of the framework in a network forensics investigative scenario is presented for demonstration. In addition, the XAI-DF project lays the basis for a collaborative effort from the forensics community, aimed at creating an open-source forensic database that may be employed to train AI models for the digital forensics domain. As an onset contribution to the project, we create a memory forensics database of 27 memory dumps (Windows 7, 10, and 11) simulating malware activity and extracting relevant features (specific to processes, injected code, network connections, API hooks, and process privileges) that may be used for training, testing, and validating AI models in keeping with the XAI-DF framework.