Azqa Nadeem


02.370, Building Echo, EEMCS,

Delft University of Technology,

The Netherlands

I am a Ph.D. candidate in the Cyber Analytics Lab (Cyber Security group) at Delft University of Technology, advised by Dr. Sicco Verwer.

In my research, I utilize Machine Learning for Cyber Security. Specifically, I develop explainable sequential machine learning pipelines that extract actionable intelligence from large volumes of cyber data with the aim of assisting security analysts in their daily operations.

Next to research, I teach introductory Cyber Security to BSc. (Computer Science and Engineering) students. The teaching material can be found under Cyber Security Lecture Series.

Outside of work, I love landscape photography and traveling. I enjoy discussions about human psychology, devious behavior, cats, imposter syndrome, and… well… cats.

Recent news

11-2022 Panelist for a webinar on MSc. scholarship opportunities in NL organized by the Pakistani embassy.
11-2022 Won the “People’s Choice Award” for our XAI-for-security SoK poster at Alice&Eve 2022! :trophy: :gift:
11-2022 Our CSE teaching team is nominated (top-3) for the TU Delft Education Team Award, 2022! :tada:
10-2022 Won the “Best Poster Award” for our alert-driven attack graph dashboard at VizSec 2022! :trophy:
08-2022 Co-chairing the Cybersecurity Next Generation (CSng 2022) workshop. :school:

Selected publications

  1. ECML
    SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids
    and Medoid Voting
    Nadeem, Azqa, and Verwer, Sicco
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2022
  2. ArXiv
    SoK: Explainable Machine Learning for Computer Security Applications
    Nadeem, Azqa, Vos, Daniel, Cao, Clinton, Pajola, Luca, Dieck, Simon, Baumgartner, Robert, and Verwer, Sicco
    In ArXiv, 2022
  3. TDSC
    Alert-driven Attack Graph Generation using S-PDFA
    Nadeem, Azqa, Verwer, Sicco, Moskal, Stephen, and Yang, Shanchieh J
    In IEEE Transcations on Dependable and Secure Computing, 2021
  4. Sec+AI
    Intelligent Malware Defenses: A survey
    Nadeem, Azqa, Rimmer, Vera, Joosen, Wouter, and Verwer, Sicco
    In Security and Artificial Intelligence, 2022
    Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of
    Malware Families
    Nadeem, Azqa, Hammerschmidt, Christian, Ganan, Carlos H, and Verwer, Sicco
    In Malware Analysis Using Artificial Intelligence and Deep Learning, 2021