Modern commercial antivirus systems increasingly rely on machine learning (ML) to keep up
with the rampant inflation of new malware. However, it is well-known that machine learning …

With the celebrated success of deep learning, some attempts to develop effective methods
for detecting malicious PowerShell programs employ neural nets in a traditional natural …

Modern commercial antivirus systems increasingly rely on machine learning to keep up with
the rampant inflation of new malware. However, it is well-known that machine learning …

We present a black-box adversarial attack algorithm which sets new state-of-the-art model
evasion rates for query efficiency in the $\ell_\infty $ and $\ell_2 $ metrics, where only loss …

Deep Learning algorithms are effectively working for detection and classification in real-time
systems. It surpasses human-level accuracy in image detection, disease classification, and …

For a long time, malware classification and analysis have been an arms-race between
antivirus systems and malware authors. Though static analysis is vulnerable to evasion …

Modern commercial antivirus systems increasingly rely on machine learning to keep up with
the rampant inflation of new malware. However, it is well-known that machine learning …

We propose MALIGN, a novel malware family detection approach inspired by genome
sequence alignment. MALIGN encodes malware using four nucleotides and then uses …

A central challenge of malware detection using machine learning methods is the presence
of adversarial variants, small changes to detectable malware that allow it to evade a model …