A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

TL;DR

This paper introduces a new malware detection method that converts malware binaries into images, extracts features, and applies machine learning classifiers to effectively distinguish malware types.

Contribution

The paper presents a novel approach using image-based feature extraction from malware binaries combined with machine learning for improved detection.

Findings

Effective differentiation of malware classes using image features

High accuracy achieved with ML classifiers on extracted features

Stealthy malware detection improved over traditional static/dynamic methods

Abstract

Our computer systems for decades have been threatened by various types of hardware and software attacks of which Malwares have been one of them. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even disrupt the entire system. There have been techniques to detect malware by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis, there have been previous works that propose different methods to detect malware but, in this work we propose a novel technique to detect malware. We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.