MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

TL;DR

MalwareDNA introduces a unified machine learning framework capable of classifying malware, identifying malware families, and detecting novel malware, addressing real-world challenges in cybersecurity detection systems.

Contribution

The paper presents a novel method that simultaneously classifies malware, detects novel malware, and unifies multiple classification tasks in a single framework.

Findings

Preliminary results show effective identification of novel malware.

Unified framework improves detection accuracy across tasks.

Addresses real-world challenges in malware detection.

Abstract

Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem. The majority of current ML approaches ignore real-world challenges such as the detection of novel malware. In addition, proposed ML approaches are often designed either for malware/benign-ware classification or malware family classification. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.