Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

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.

The twofold role of Cloud Computing in Digital Forensics: target of investigations and helping hand to evidence analysis

This PhD thesis discusses the impact of Cloud Computing infrastructures on Digital Forensics in the twofold role of target of investigations and as a helping hand to investigators. The Cloud offers a cheap and almost limitless computing power and storage space for data which can be leveraged to commit either new or old crimes and host related traces. Conversely, the Cloud can help forensic examiners to find clues better and earlier than traditional analysis applications, thanks to its dramatically improved evidence processing capabilities. In both cases, a new arsenal of software tools needs to be made available. The development of this novel weaponry and its technical and legal implications from the point of view of repeatability of technical assessments is discussed throughout the following pages and constitutes the unprecedented contribution of this work

The book of Qohelet. A digital scholarly edition of the hebrew text

The objective of the present dissertation is a born-digital critical edition of the Hebrew Old Testament book of Qohelet. The edition is based on an extensive collation of variant readings from indirect sources – the Septuagint, the Peshitta, the works of St. Jerome (the Vulgate and the Commentary), and the Targum – as well as from direct sources such as the Qumran fragments and Hebrew medieval manuscripts. The ultimate goal of the edition is (a) to reproduce the earliest textual form, the Archetype, that can be reconstructed on the basis of the available evidence; and (b) to propose a rehabilitation of the Original of the Author by resorting, when necessary, to conjectural emendation. We date the Archetype to the II century BCE, corresponding to the date of Hebrew fragments from Qumran, while we place the Original between the V and III centuries BCE. Unlike previous critical editions of Qohelet, ours follows the so-called eclectic model, which involves the reconstitution of a critical text and the preparation of an apparatus of secondary variants. Our edition includes, moreover, new data, taken both from primary literature, such as the recently published Göttingen Septuagint, and from up-to-date studies and critical commentaries on the text of Qohelet. The work is made up of five main parts: an introduction, which sets forth the rationale of the edition and the methodology adopted; the collation, where the variants are listed in their original language; the commentary, where they are extensively discussed; the critical text accompanied by the apparatus, which presents a selection of authentic Hebrew variants taken from the collation; and finally, a translation of the critical text. The edition uses the mark-up language of the Text Encoding Initiative (TEI). It is realized in pdf, via LaTeX, and will be available in digital form, via the TEI-Publisher editor.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.