Reference : Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Ev...
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/52127
Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques
English
Solanke, Abiodun Abdullahi mailto [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > >]
17-Jun-2022
University of Luxembourg, ​Esch-Sur-Alzette, ​​Luxembourg
Docteur en Informatique
236
Sjouke, Mauw mailto
Biasiotti, Maria Angela mailto
Perri, Pierluigi mailto
Ferretti, Stefano mailto
Pietropaoli, Stefano mailto
[en] Digital Forensics AI ; Evidence Mining ; Digital Forensics ; Explainable Digital Forensics AI (xDFAI) ; Interpretable AI ; E-mail Artifacts ; VGAE ; Latent Dirichlet Allocation (LDA) ; Non-Matrix Factorization (NMF) ; Topic Modelling ; Natural Language Processing (NLP) ; Graph Neural Network (GNN)
[en] Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/52127

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