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 …

This paper describes EMBER: a labeled benchmark dataset for training machine learning
models to statically detect malicious Windows portable executable files. The dataset …

Binary code fingerprinting is crucial in many security applications. Examples include
malware detection, software infringement, vulnerability analysis, and digital forensics. It is …

Modern malware is designed with mutation characteristics, namely polymorphism and
metamorphism, which causes an enormous growth in the number of variants of malware …

Recent statistics show that in 2015 more than 140 millions new malware samples have been
found. Among these, a large portion is due to ransomware, the class of malware whose …

For the past two decades, the security community has been fighting malicious programs for
Windows-based operating systems. However, the recent surge in adoption of embedded …

Machine learning techniques are widely used in addition to signatures and heuristics to
increase the detection rate of anti-malware software, as they automate the creation of …

Malware classification using machine learning algorithms is a difficult task, in part due to the
absence of strong natural features in raw executable binary files. Byte n-grams previously …

Many efforts have been made to use various forms of domain knowledge in malware
detection. Currently there exist two common approaches to malware detection without …

The voluminous malware variants that appear in the Internet have posed severe threats to its
security. In this work, we explore techniques that can automatically classify malware variants …