Offensive Applications of Machine Learning in Cybersecurity
As artificial intelligence and machine learning become deeply embedded in security-critical systems, their dual-use nature raises questions about how these technologies can be used …
Professional Summary
Maria Rigaki is a post-doctoral researcher in the Department of Computer Science at the Czech Technical University in Prague. As a member of the Stratosphere Lab, she works on the security and privacy of machine learning, and on applications of AI in cyber security. Before that she spent many years as a software developer and systems architect, working on telecommunications, physical security, emergency response systems and critical infrastructure.
In her spare time Maria enjoys hacking and playing with guitars.
Education
PhD in Computer Science
Czech Technical University in Prague
MSc in Information Security
2015-08-01
2017-07-31
Luleå University of Technology, Sweden
Diploma in Electrical Engineering and Computer Science
University of Patras, Greece
Interests
Applications of ML in systems security
Privacy and Security of Machine Learning
Network Security
Malware Analysis
CTFs
OSINT
Featured Publications
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection tool-chain. However, machine learning …
Recent Publications
(2026).
Building Adaptative and Transparent Cyber Agents with Local Language Models.
Expert Systems with Applications, 299.
(2026).
Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents.
arXiv preprint.
(2026).
AlquistCoder: A Synthetic Data Approach to Training Compact Secure Coding Assistants and Building Security Benchmarks.
Computational Intelligence, 42(4).
(2025).
Offensive Applications of Machine Learning in Cybersecurity.
PhD Dissertation, Czech Technical University in Prague.
(2024).
Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments.
arXiv preprint.
Recent & Upcoming Talks
Stealing Malware Classification Models for Antivirus Evasion
Model stealing attacks have been successfully demonstrated in several domains. However, in the area of malware detection, there is no comparison of surrogate creation strategies, …
Forget me not: Malware Classification as a Continual Learning Problem
It is well known in the machine learning community that the performance of models degrades over time with the introduction of new data, getting worse in solving both new and old …
Virus Bulletin 2019
Reports on cyber espionage operations have been on the rise in the last decade. However, operations in Latin America are heavily under researched and potentially underestimated. In …
veronica-valeros
Arming Malware with GANs
Generative Adversarial Networks (GANs) are a recent invention that shows impressive results in generating completely new images of faces, building interiors and much more. In this …
Recent News
Changing Weights and Biases Programmatically for "Neural Network Hacking" and more
TL;DR Using Tensorflow / Keras APIs to read and change Neural Network parameters. A few days ago I came across a paper called “Hacking Neural Networks” by Michael Kissner aka …
Scoping it out!
TL;DR Using a Picoscope 2204A and its SDK to perform timing side channel attacks against a weak password checker implementation running in an Arduino. Code, screenshots and lessons …