30 citations · 54 across the 13 of their papers we have counts for
11 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…
Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
Matouš Kozák, Martin Jureček
Adversarial attacks present significant challenges for malware detection systems. This research investigates the effectiveness of benign and malicious adversarial examples (AEs) in…
Online Clustering of Known and Emerging Malware Families
Olha Jurečková, Martin Jureček, Mark Stamp
Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information se…
A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová, Matouš Kozák, Martin Jureček +1
Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks. This arti…
Creating Valid Adversarial Examples of Malware
Matouš Kozák, Martin Jureček, Mark Stamp +1
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine lea…
Classification and Online Clustering of Zero-Day Malware
Olha Jurečková, Martin Jureček, Mark Stamp +2
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose,…
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
Matouš Kozák, Martin Jureček
Antivirus developers are increasingly embracing machine learning as a key component of malware defense. While machine learning achieves cutting-edge outcomes in many fields, it als…
Parallel Instance Filtering for Malware Detection
Martin Jureček, Olha Jurečková
Machine learning algorithms are widely used in the area of malware detection. With the growth of sample amounts, training of classification algorithms becomes more and more expensi…