Professor of Computer ScienceDepartment of Information Security and Communication Technology Faculty of Information Technology and Electrical Engineering
Background and activities
Katrin Franke is a professor in computer science within the information security environment at NTNU in Gjøvik. In 2007 she joined the Norwegian Information Security Lab (NISlab) with the mission to establish research and education in digital and computational forensics. In this context she was instrumental in setting up the partnership with the Norwegian police organisations as part of the Center for Cyber and information Security (CCIS). Dr. Franke is now leading the NTNU Digital Forensics group. Dr. Franke has 20+ years experiences in basic and applied research for financial services & law enforcement agencies (LEAs) working closely with banks and LEAs in Europe, North America and Asia.
- IMT4205 - Research Project Planning
- IMT6101 - Computational Intelligence
- IMT6091 - Computational Forensics
- IMT4210 - Computational Forensics
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2022) Quantifying data volatility for IoT forensics with examples from Contiki OS. Forensic Science International: Digital Investigation. vol. 40.
- (2022) Reliability assessment of digital forensic investigations in the Norwegian police. Forensic Science International: Digital Investigation. vol. 40.
- (2021) Reliability validation for file system interpretation. Forensic Science International: Digital Investigation. vol. 37.
- (2021) Coffee forensics — Reconstructing data in IoT devices running Contiki OS. Forensic Science International: Digital Investigation. vol. 37.
- (2020) Generic Metadata Time Carving. Forensic Science International: Digital Investigation. vol. 33.
- (2019) Identifying Anomalous HTTP Traffic with Association Rule Mining. International Symposium on Advanced Networks and Telecommunication Systems (ANTS). vol. 2019-.
- (2019) The impact of preprocessing in natural language for open source intelligence and criminal investigation. TEMP 2017 IEEE International Conference on Big Data (Big Data).
- (2018) Comparing Open Source Search Engine Functionality, Efficiency and Effectiveness with Respect to Digital Forensic Search. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 11.
- (2018) Identifying Central Individuals in Organised Criminal Groups and Underground Marketplaces. Lecture Notes in Computer Science (LNCS). vol. 10862 LNCS.
- (2017) Feasibility Study of Social Network Analysis on Loosely Structured Communication Networks. Procedia Computer Science. vol. 108.
- (2017) Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness. Applied Soft Computing. vol. 52.
- (2016) Data-driven Approach to Information Sharing using Data Fusion and Machine Learning for Intrusion Detection. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 2016.
- (2016) Memory access patterns for malware detection. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 2016.
- (2016) Intelligent generation of fuzzy rules for network firewalls based on the analysis of large-scale network traffic dumps. International Journal of Hybrid Intelligent Systems. vol. 13 (3-4).
- (2016) Multinomial classification of web attacks using improved fuzzy rules learning by Neuro-Fuzzy. International Journal of Hybrid Intelligent Systems. vol. 13 (1).
- (2016) Malware Beaconing Detection by Mining Large-scale DNS Logs for Targeted Attack Identification. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering. vol. 10 (4).
- (2014) Practical use of Approximate Hash Based Matching in digital investigations. Digital Investigation. The International Journal of Digital Forensics and Incident Response. vol. 11 (May).
- (2013) Enhancing the effectiveness of Web Application Firewalls by generic feature selection. Logic Journal of the IGPL. vol. 21 (4).
- (2012) Clustering Document Fragments using Background Color and Texture Information. Proceedings of SPIE, the International Society for Optical Engineering. vol. 8297.
- (2012) Combining expert knowledge with automatic feature extraction for reliable web attack detection. Security and Communication Networks.