Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …

Malware is one of the prevalent security threats. Sandboxes and, more generally,
instrumented environments play a crucial role in dynamically analyzing malware samples …

Ransomware, the malicious software that encrypts user files to demand a ransom payment,
is one of the most common and persistent threats. Cyber-criminals create new ransomware …

We present PROVNINJA, a framework designed to generate adversarial attacks that aim to
elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

The continuous proliferation of malware poses a formidable threat to the cyberspace
landscape. Researchers have proffered a multitude of sophisticated defense mechanisms …

Antiviruses (AVs) are computing-intensive applications that rely on constant monitoring of
OS events and on applying pattern matching procedures on binaries to detect malware. In …

Malicious activities on the Internet continue to grow in volume and damage, posing a serious
risk to society. Malware with remote control capabilities is considered one of the most …

Analyzing malware based on API call sequence is an effective approach as the sequence
reflects the dynamic execution behavior of malware. Recent advancements in deep learning …