Identification of Cyber Threats using Machine Learning - DigitalDays Event

Cybersecurity consists of identifying patterns in software systems which are caused by malicious activity. These patterns---also known as malware signatures or more generally, as Indicators of Compromise (IoCs)---are typically identified from experience. I.e., it is only once a malicious actor has caused damage that its associated IoC can be extracted and stored in a repository of intelligence. 

Subsequent attacks from that same threat can then be thwarted by looking up the corpus of intelligence for the matching IoCs. This paradigm based on static look-ups can be augmented by heuristics so as to "bootstrap" the intelligence to new, out-of-sample threats. This is however demanding in terms of domain knowledge and prone to obsolescence. To overcome these limitations, machine learning (ML) presents itself as the ultimate improvement in that it generates its own heuristics on the fly.

This talk will present a joint research project---supported by the Innovation Fund---between the CSIS, DTU, and AAU, which aims to lay the groundwork for a unifying approach to applying ML to cybersecurity. The target system to result from this project shall incorporate a way to elicit and relate the various features needed to generate business intelligence. This presents a host of technical, scientific, and organizational challenges that we will discuss.

Del på LinkedIn
17. juni 2021
Kl. 10:15 - 11:15


Alle priser er DKK og ekskl. moms
Iben Bondegaard Andersen
+45 20 77 16 37
Tilmeldingsfristen er desværre overskredet. Kontakt DigitalLead for tilmelding.


16. juni 2021 kl. 00:00




16. juni 2021 kl. 00:00


  • DigitalLead

Amine Laghaout holds an Hon. B.Sc. in Computer Science from the University of Toronto, a M.Sc. in physics from the Royal Institute of Technology (KTH), and a Ph.D. in quantum physics from the Technical University of Denmark (DTU).

Christian D. Jensen holds a Ph.D. in computer science from Université Joseph Fourier (Grenoble, France), an M.Sc. in computer science from the University of Copenhagen (Denmark), an eMBA from the Technical University of Denmark and an M.A. (jure officii) from Trinity College Dublin (Ireland). He is an associate professor at the Department of Applied Mathematics & Computer Science at the Technical University of Denmark, where he heads the Cyber Security Research Section.

LinkedIn logo Amine Laghaout

Amine Laghaout

Machine learning scientist, CSIS Security Group
LinkedIn logo Christian Damsgaard Jensen

Christian Damsgaard Jensen

Associate Professor & Head of Cyber Security Section, DTU
DTU - Danmarks Tekniske Universitet

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