PhD Candidates
Akila Wickramasekara
Forensics and Security Research Group
Akila Wickramasekara is a PhD candidate in the Forensics and Security Research Group. A fuller biography, research profile, and headshot will be added soon.
Research Output
Publications
AutoDFBench 1.0: A benchmarking framework for digital forensic tool testing and generated code evaluation
Forensic Science International: Digital Investigation Vol. 56 pp. 302055
AutoDFBench 1.0 is a benchmarking framework for digital forensic tool testing, evaluating conventional and AI-generated tools across five areas: string search, deleted file recovery, file carving, Windows registry recovery, and SQLite data recovery.
AutoDFBench: A Framework for AI Generated Digital Forensic Code and Tool Testing and Evaluation
Digital Forensics Doctoral Symposium
AutoDFBench is an automated framework for testing and evaluating AI-generated digital forensic code and tools. It validates AI-generated code against NIST's Computer Forensics Tool Testing Program (CFTT) procedures and calculates a benchmarking score. The framework operates in four phases: data preparation, API handling, code execution, and result recording with score calculation.
Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
Forensic Science International: Digital Investigation Vol. 52 pp. 301859
This study explores the potential of Large Language Models (LLMs) in improving digital forensic investigation efficiency, addressing challenges such as bias, explainability, censorship, and resource-intensive infrastructure. A comprehensive literature review highlights the current challenges in digital forensics and the possibilities of incorporating LLMs, with a focus on established models, methods, and key challenges.
A Framework for Integrated Digital Forensic Investigation Employing AutoGen AI Agents
Proceedings of the 12th International Symposium on Digital Forensics and Security
This paper proposes an integrated framework for digital forensic investigations employing AutoGen AI agents and Large Language Models (LLMs) to alleviate investigative workload and shorten the learning curve for investigators. The framework utilizes AI agents and LLMs to perform tasks articulated in natural language by a human agent, addressing the challenges of evolving requirements and information accuracy.