5 papers
Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
Lukáš Hrdonka, Martin JureÄek
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 JureÄek, 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 JureÄková, Martin JureÄek, MatouÅ¡ 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 JureÄková, Martin JureÄek
Malware detection and classification into families are critical tasks in cybersecurity, complicated by the continual evolution of malware to evade detection. This evolution introdu…
Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
MatouÅ¡ Kozák, Martin JureÄek
Adversarial attacks present significant challenges for malware detection systems. This research investigates the effectiveness of benign and malicious adversarial examples (AEs) in…