Teaching

Winter 2026

NI(E)-AIB Algorithms of Information Security (in Slovak, English)

Summer 2027

NI(E)-MKY Mathematics for Cryptology (in Slovak, English)

Teaching Experience

Doctoral theses

Matouš Kozák, Adversarial Learning Techniques for Malware Detection, expected 2027
Benjamín Peraus, Reducing the Surface for Adversarial Attacks in Malware Detectors, expected 2028
Jan Dolejš, Defense Against Adversarial Attacks for Malware Detection, expected 2029
Lukáš Hrdonka, Multimodal Representation Learning for Malware Analysis and Detection, expected 2030
Pavel Holý, Robust Malware Detection under Adversarial Evolution and Family Drift, expected 2030
David Bálik, Modeling and Prediction of Cyber Threat Actor Behavior, expected 2030

Master theses

Damián Baraník, Adversarial Malware Generator Based on Generative Adversarial Network, expected 2027
Martin Heinrich, Adversarial Malware Obfuscation via Semantic-Preserving Instruction Transformations, expected 2027
Lukáš Hrdonka, Robust Adversarial Attacks on Linux ELF Binaries: Evaluating and Bypassing ML-Based Malware Detectors, defended 2026, Dean's Award
David Košťál, Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation, defended 2026
Radek Večerník, Robust Malware Detection Under Adversarial Packing: Analysis and Mitigation of Static ML Weaknesses, defended 2026
Tomáš Kalný, Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifiers, defended 2026
Pavel Holý, Comparative Evaluation of Open-Source Implementations of Gröbner Basis Computation Algorithms for Small-Scale AES Polynomial Systems over GF(2), defended 2026, Dean's Award
David Bálik, Machine Learning-Based Malware Classification by Type and Family, defended 2025, Dean's Award
Tomáš Faikl, Algebraic Cryptanalysis of Small Scale Variants of the AES Based on Lattice Basis Reduction, defended 2025
Benjamín Peraus, Reducing the Surface for Adversarial Attacks in Malware Detectors, defended 2024
Lukáš Děd, Selection of Representative Samples from Datasets for Malware Detection E0, defended 2024, Dean's Award
Jan Dolejš, Algebraic Cryptanalysis of Small-Scale Variants of Stream Cipher E0, defended 2024, Dean's Award
Mehmet Efe Zorlutuna, Unsupersived Instance Selection for Malware Detection, defended 2023
Daniel Minarovič, Algebraic Cryptanalysis of Small Scale Variants of the Grain (in Slovak), defended 2023
Pavla Louthánová, A Comparison of Adversarial Learning Techniques for Malware Detection, defended 2023, Dean's Award
Matouš Kozák, Application of Reinforcement Learning to Creating Adversarial Malware Samples, defended 2023, Dean's Award
Jana Berušková, Reducing Overdefined Systems of Polynomial Equations Derived from Small Scale Variants of the AES (in Czech), defended 2023
Tomáš Zvára, Generating Malware Family Signatures from Behavioral Graphs using Unsupervised Learning, defended 2022
Jiří Soukup, Algebraic Cryptanalysis of LFSR-based Stream Ciphers (in Czech), defended 2022
Marek Bielik, Algebraic Cryptanalysis of Small Scale Variants of the AES, defended 2021, SVOČ 2021 third place
Libor Šlechta, Simulation of Malware Detection Model (in Czech), defended 2020
Michal Buchovecký, Semi-supervised Learning for Malware Detection (in Slovak), defended 2019

Bachelor theses

Maroš Letaši, Verifying ML-KEM key exchange with STARK Zero Knowledge Proofs, defended 2026
Marek Holík, Hard Mathematical Problems in Cryptography, defended 2022
Jan Dolejš, Interpretability of Machine Learning-based Results of Malware Detection using a Set of Rules, defended 2021, Dean's Award
Petr Horák, Cryptanalysis of RSA Based on Factorization, defended 2020
Matouš Kozák, Static Malware Detection Using Recurrent Neural Networks, defended 2020
Jakub Ács, Static Detection of Malicious PE Files, defended 2018
Tomáš Hradský, Real Time Attack of A5/1 (in Czech), defended 2015
Martin Holec, Correlation Attack on A5/1, defended 2015