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| Dato | Tittel | Foreleser | Emne | |
|---|---|---|---|---|
 |
9. oktober 2017 Kl. 13.15 255 min Lille Eureka, 1/3 Eureka |
The Digital Forensics Process and Forensic Readiness Extra 30 minutes added to end of session, in case of over-runs |
Carl Stuart Leichter | IMT 4114 Introduction to Digital Forensics |
 |
4. oktober 2017 Kl. 16.15 240 min A153 |
Lab day 2 | Carl Stuart Leichter | IMT4114 Introduction to Digital Forensics |
 |
6. oktober 2017 Kl. 14.15 240 min A153 |
Lab day 4 | Carl Stuart Leichter | IMT4114 Introduction to Digital Forensics |
 |
4. september 2017 Kl. 13.15 225 min Lille Eureka, 1/3 Eureka |
IMT4114 first lecture Introduction to digital Forensics |
Carl Stuart Leichter | IMT4114 Introduction to Digital Forensics |
 |
23. oktober 2017 Kl. 13.15 255 min D101 |
Mobile/Embedded and Internet Forensics 30 Minutes extra time added to recording duration, in case of over-runs. |
Carl Stuart Leichter | IMT4114 Introduction to Digital Forensics |
 |
15. september 2017 Kl. 12.12 55 min A146 |
Big Data Analytics: Topic Modeling for Digital Forensics Investigations and Cyber Threat Intelligence “Big Data Analytics” has become a high priority topic in Cyber Research and in the field of Cyber Security, Big Data represents a very serious problem. In the domain of Digital Forensics Investigations (DFI), the sheer volume of data to be analyzed impedes police operations that require timely reporting of DFI results to support active criminal investigations in the field. In the domain of Cyber Threat Intelligence (CTI), a rapid assessment of the available threat data is required to enable dissemination of actionable intelligence in a timely manner. Topic Modeling is an unsupervised machine learning method for analyzing large bodies of text data and producing estimates of the topics under discussion in them. To gain some insight into how it works, we reviewed some of the underlying principles of Topic Modeling. Then, I presented experimental results that show how Topic Modeling would work in the specific domains of DFI (using the Enron data set) and CTI (using posts scraped from an online hacker forum). |
Carl Stuart Leichter | NISseminar |