A Kullback-Liebler divergence-based representation algorithm for malware detection.

ABSTRACT:

Background

Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those behaviors as malicious or legitimate activities. For instance, evasive malware often mimics legitimate behaviors, and evasion techniques are utilized by legitimate and malicious software.

Problem

Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately beca

SUBMITTER: Aboaoja FA 

PROVIDER: S-EPMC10557483 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature