Coping with malware is getting more and more challenging, given their relentless growth in
complexity and volume. One of the most common approaches in literature is using machine …

Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

In this work we introduce malware detection from raw byte sequences as a fruitful research
area to the larger machine learning community. Building a neural network for such a …

In this paper we introduce a deep neural network based malware detection system that
Invincea has developed, which achieves a usable detection rate at an extremely low false …

Labeling a malicious executable as a variant of a known family is important for security
applications such as triage, lineage, and for building reference datasets in turn used for …

As embedded systems are more than ever present in our society, their security is becoming
an increasingly important issue. However, based on the results of many recent analyses of …

This paper introduces AMAL, an automated and behavior-based malware analysis and
labeling system that addresses shortcomings of the existing systems. AMAL consists of two …

(51) Int. Cl.(57) ABSTRACT G06F II/00(2006.01) A computerized method is described in
which a received G06F 2/4(2006.01) object is analyzed by a malicious content detection …

The Android packaging model offers ample opportunities for malware writers to piggyback
malicious code in popular apps, which can then be easily spread to a large user base …