IMT4210 - Computational Forensics


Examination arrangement

Examination arrangement: Assignment (essay/project)
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Assignment (essay/project) 100/100 ALLE

Course content

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.

Learning outcome

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.

Learning methods and activities

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

Further on evaluation

Re-sit: The whole course must be repeated the next time the course is running.

Forms of assessment:

- The students must deliver an "essay", or an essay and supporting project (source code for the experiment, for example). This essay should be a research manuscript regarding the subject chosen by the student (we will offer a number of possible subjects, and opportunities for discussion). If the student choses to only write an essay, the essay will count towards 100% of the student's grade. The expectation is that the essay will be a research survey of their given subject and be roughly 15-20 pages. If the student choses to write an essay and deliver a project, the essay will count for 50% of the grade, and the project will count towards 50% of the grade (both parts must be passed). The expectation is that the essay will be an empirical research article in which the project essentially acts as source code for their methodology and analysis, where the length of the paper should be roughly 10 pages.

- The students must present their work as short presentations to the class three times during the course. The presentations will be held near the start of the semester (expected work, research questions, potential data sources and methods), in the middle of the semester (ongoing work, adjustments), and at the end of the semester (essentially finalized research questions, and results). If the students cannot attend the presentations live, then they must send a recording of the presentations. Failure to present or send presentations will result in the reduction of the student's final grade.

Specific conditions

Admission to a programme of study is required:
Information Security (MIS)
Information Security (MISD)
Information Security (MISEB)

Required previous knowledge

Candidates must have passed IMT4133 Data Science for Security and Forensics.

Course materials

Scientific Articles related to the field of Specialization.

Credit reductions

Course code Reduction From To
IMT4641 5.0 AUTUMN 2017
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Information Security
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Information Security and Communication Technology


Examination arrangement: Assignment (essay/project)

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Assignment (essay/project) 100/100 ALLE INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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