The Ars Forensica project on Computational Forensics for Large-scale Fraud Detection, Crime Investigation & Prevention is funded by the Research Council of Norway for the period 2015-2019.

The overall objective of Ars Forensica is to provide new knowledge that can significantly improve the prevention, preparedness, investigation and prosecution of incidents in ICT environments, without compromising privacy and the rule of law. Ars Forensica addresses topics related to the Research Council of Norway (RCN) IKTPLUSS – programme:

1.      Robust and secure (ICT) infrastructures and systems;

2.      Privacy-preserving technologies, and

3.      Interaction between technology, individuals and communities.

Project partners

  • Norwegian Computing Center
  • The Norwegian Police University College (PHS)
  • The Norwegian Police Directorate (POD)
  • The Norwegian National Authority for Investigation and Prosecution of Economic and Environmental Crime (ØKOKRIM)
  • Oslo Police District

Industry partners

  • FinansCERT
  • mnemonic

International cooperation

  • Netherlands Forensics Institute (NFI)
  • United Nations Interregional Crime and Justice Research Institute (UNICRI)
  • Synergetics

International academic partners

  • University of California Santa Cruz (UCSC), USA
  • Kyushu Institute of Technology (Kyutech), Japan
  • University of Groningen (RUG) - Security, Technology and e-Privacy Research Group (STEP), The Netherlands

Digital investigations in the Norwegian financial sector

This project focuses on proactive and reactive digital investigations in the Norwegian financial sector in order to prevent and combat fraud, economic crime, money laundering and terror finance. Fraud prevention and criminal investigations lead to exciting research challenges, i.e.

  1. Huge amount of electronic data needs to be analysed
  2. Tiny pieces of evidence that are hidden in a chaotic environment
  3. Diverse quality of traces and possibility of obfuscating / planting
  4. Dynamic environments and permanently changing situations / contexts
  5. Partial knowledge, required approximation
  6. Decision making under uncertainties and conjectures


Project leader

Professor Katrin Franke,

Project coordinator

Maria Henningsson,, +47 90578914

Project members

Overview project members

Digital Forensics Textbook (Wiley 2017)

In July 2017 our Digital Forensics textbook was published by Wiley ( This is a textbook for students of digital forensics, as well as professionals looking to deepen their understanding of an increasingly critical field. Written by faculty members and associates of the NTNU Digital Forensics Group, this textbook takes a scientific approach to digital forensics ideally suited for university courses in digital forensics and in digital forensics and information security. The author team comprises experts in digital forensics, cybercrime law, information security and related areas. In particular, Ars Forensica project members, Stefan Axelsson, Katrin Franke, Jens-Petter Sandvik, Inger Marie Sunde, and Andre Årnes have authored chapters in the book "Digital Forensics”. For details see below.


Årnes, André (Editor). “Digital Forensics”, Wiley 2017, ISBN: 978-1-119-26238-1, (


  • Årnes, André. “Introduction – Forensic Science, Digital Forensics, Digital Evidence”, Digital Forensics, Wiley 2017, ISBN: 978-1-119-26238-1, pp. 1-12.
  • Sunde, Inger Marie. “Cybercrime Law“: Digital Forensics, Wiley 2017, ISBN: 978-1-119-26238-1, pp. 51-116.
  • Sandvik, Jens-Petter. “Mobile and Embedded Forensics”: Digital Forensics, Wiley 2017, ISBN: 978-1-119-26238-1, pp. 191-274.
  • Franke, Katrin; Årnes, André. “Challenges in Digital Forensics“: Wiley 2017, ISBN: 978-1-119-26238-1, pp. 313-318.
  • Axelsson, Stefan. “Educational Guide“: Digital Forensics, Wiley 2017, ISBN: 978-1-119-26238-1, pp. 319-324.


Journal Articles

  • Shalaginov, Andrii; Franke, Katrin: "Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness", Applied Soft Computing, 2017, vol. 52, pp. 359-375.
  • Årnes, André; Jensen, Kristoffer; Van Do, Thanh; Nguyen, Hai Thanh. "A Big Data Analytics Approach to Combat Telecommunication Vulnerabilities", Cluster Computing, 2017, Springer US, pp. 1-12.

Conference Papers

  • Andersen, Lars Christian; Franke, Katrin; Shalaginov, Andrii. "Data-driven Approach to Information Sharing using Data Fusion and Machine Learning for Intrusion Detection", Norsk informasjonssikkerhetskonferanse (NISK), Volume 2016, pp. 19-30.
  • Banin, Sergii; Shalaginov, Andrii; Franke, Katrin. "Memory access patterns for malware detection", Norsk informasjonssikkerhetskonferanse (NISK), Volume 2016, pp. 96-107.
  • Chitrakar, Ambika Shrestha; Petrovic, Slobodan. "Constrained Row-Based Bit-Parallel Search in Intrusion Detection", Norsk Informasjonssikkerhetskonferanse (NISK), Volume 2016, pp. 68-79.
  • Johnsen, Jan William; Franke, Katrin. “Feasibility Study of Social Network Analysis on Loosely Structured Communication Networks”, in Procedia Computer Science, Volume 108, 2017, pp. 2388-2392, ISSN 1877-0509.
  • Petrovic, Slobodan. “A SPAM Filtering Scenario Using Constrained Bit-Parallel Approximate Search”, XIV Spanish Meeting on Cryptology and Information Security (RECSI0), 2016, pp. 186-190.
  • Shalaginov, Andrii. "Soft Computing and Hybrid Intelligence for Decision Support in Forensics Science", IEEE Intelligence and Security Informatics, 2016.
  • Shalaginov, Andrii; Franke, Katrin. "Automated intelligent multinomial classification of malware species using dynamic behavioural analysis", IEEE Privacy, Security and Trust, 2016.
  • Shalaginov, Andrii: "Evolutionary optimization of on-line multilayer perceptron for similarity-based access control", International Joint Conference on Neural Networks (IJCNN), 2017.
  • Shalaginov, Andrii: "Dynamic feature-based expansion of fuzzy sets in Neuro-Fuzzy for proactive malware detection", 20th International Conference on Information Fusion (Fusion), 2017.
  • Shalaginov, Andrii: "Fuzzy Logic Model for Digital Forensics: A Trade-off between Accuracy, Complexity and Interpretability", International Joint Conferences on Artificial Intelligence (IJCAI), 2017.
  • Årnes, André; Jensen, Kristoffer; Van Do, Thanh; Nguyen, Hai Thanh. “Better Protection of SS7 Networks with Machine Learning”, IT Convergence and Security (ICITCS), IEEE 6th International Conference, 2016, pp. 1-7.