Malware has been one of the most damaging threats to computers that span across multiple
operating systems and various file formats. To defend against ever-increasing and ever …

Machine learning has become widely adopted as a strategy for dealing with a variety of
cybersecurity issues, ranging from insider threat detection to intrusion and malware …

Malware detection is one of the most important tasks in cybersecurity. Recently, increasing
interest in Convolutional Neural Networks (CNN) and Machine Learning algorithms, which …

Abstract Model stealing attacks have been successfully used in many machine learning
domains, but there is little understanding of how these attacks work against models that …

Static analysis is a crucial protection layer that enables modern antivirus systems to address
the rampant proliferation of malware. These systems are increasingly relying on deep neural …

Purpose The purpose of this paper is to highlight the key technical challenges that derive
from the recently proposed European Artificial Intelligence Act and specifically, to investigate …

Antivirus vendors and the research community employ Machine Learning (ML) or Deep
Learning (DL)-based static analysis techniques for efficient identification of new threats …

Over the years, most research towards defenses against adversarial attacks on machine
learning models has been in the image recognition domain. The ML-based malware …

Reverse engineers benefit from the presence of identifiers such as function names in a
binary, but usually these are removed for release. Training a machine learning model to …

Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection
(AMD); however, ML models are vulnerable to realistic evasion attacks—crafting realizable …