Paper

this paper proposes FGAM which iterates perturbed bytes according to the gradient sign to enhance adversarial capability of the perturbed...

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance.

In this paper, the authors propose a method that involves extracting features from malware executable files and representing them as...

This survey aims to fill that gap by providing an extensive review of existing studies, focusing on common approaches and...

This paper presents Enviral, an automatic framework for evasive malware analysis that combines the strengths of various approaches.

This work reflects the difference in the decision-making process of humans and computer algorithms and the different ways they extract...

Two basic approaches were proposed: based on the signature and the heuristics rule detected, we can detect known malware accurately.

This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches.

Two basic approaches were proposed: based on the signature and the heuristics rule detected, we can detect known malware accurately.

The test used for this test consisted of 10,015 malware samples, assembled after consulting telemetry data with the aim of...