30 citations · 54 across the 13 of their papers we have counts for
6 papers · 1 filter
Social Media Bot Detection using Dropout-GAN
Anant Shukla, Martin Jurecek, Mark Stamp
Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot…
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…
Keystroke Dynamics for User Identification
Atharva Sharma, Martin Jureček, Mark Stamp
In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging…
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…