30 citations · 54 across the 13 of their papers we have counts for
4 papers · 1 filter
Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
Lukáš Hrdonka, Martin Jureček
Malware development and detection have undergone significant changes in recent years as modern concepts, such as machine learning, have been used for both adversarial attacks and d…
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Tomáš Kalný, Martin Jureček, Mark Stamp
This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the E…
Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Olha Jurečková, Martin Jureček, Matouš Kozák +1
Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adapti…
Detecting and Explaining Malware Family Evolution Using Rule-Based Drift Analysis
Olha Jurečková, Martin Jureček
Malware detection and classification into families are critical tasks in cybersecurity, complicated by the continual evolution of malware to evade detection. This evolution introdu…