Malware classification is an important and challenging problem in information security.
Modern malware classification techniques rely on machine learning models that can be …

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 different machine learning and deep learning techniques have been successfully
employed for malware detection and classification. Examples of popular learning techniques …

Recent years have witnessed the very rapid increase in both the volume and sophistication
of malware programs. Malware authors invest heavily in technologies and capabilities to …

Word embeddings are often used in natural language processing as a means to quantify
relationships between words. More generally, these same word embedding techniques can …

Malware, short for Malicious Software, is growing continuously in numbers and
sophistication as our digital world continuous to grow. It is a very serious problem and many …

The number of malicious software applications, or malware programs, increases every year.
Their development becomes more sophisticated as new techniques are used to bypass …

Signature-based malware detection approaches are inadequate for detecting the
increasingly intelligent and large number of malware programs emerging today. Therefore …

Malicious software (malware) has been extensively employed for illegal purposes and
thousands of new samples are discovered every day. The ability to classify samples with …

Research in the field of malware classification often relies on machine learning models that
are trained on high-level features, such as opcodes, function calls, and control flow graphs …