Computational Forensics

This course is intended to deepen knowledge and skills for machine learning/data science (AI) with an application to digital forensics and investigation, as well as information security. Furthermore, the course is an opportunity to gain experience in writing research articles within these fields, and presenting ongoing research to the class. Students will be given a number of different choices of subjects to research, or they may come up with a subject of their own.

Incoming students are expected to have knowledge of the basics of machine learning and digital forensics/investigation.

The course content is taught as a series of lectures and tutorials, wherein each session will focus on a specific subject. These subjects include (but are not limited to): legal considerations for AI and data when conducting forensics/investigation research, image classification, state-of-the-art and applied natural language processing, uses of deep learning, and data visualization.

Knowledge: - Understanding of cutting-edge problems in applications of AI to digital forensics and investigations (including but not limited to biometric identification, multimedia content analysis, and internet investigations). -Understanding of relevant research journals, conferences, and datasets.

Skills: - The students can use relevant scientific methods in independent research and development in applying AI to forensics and investigation. - The students are capable of carrying out an independent limited research project with supervising support and guidance from the course teachers, following the applicable ethical rules. -The students are expected to learn how to write strong research articles. -Application and use of at least one state-of-the-art AI method for the student's chosen research subject. -Ability to concisely present research and results.

General competence: - By the end of the course, candidates should be able to work independently and have the beginning capabilities for conducting and writing research articles applying AI to digital forensics or investigation. This includes having an understanding of developing research questions for their chosen research subject, and conducting empirical experiments or research surveys to answer those questions (such questions do not need to be novel). The students should have the ability to present -The candidates should also have a strong idea of what state-of-the-art research looks like for AI applications to for a variety of domains within digital forensics and investigation.

• Lectures
• Tutorials
• E-learning
• Project work
• Student Presentations