A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images
This addresses malware threats to computer systems, but appears incremental as it builds on existing image-based analysis methods.
The paper tackles malware detection by converting malware binaries to images and extracting features for classification with machine learning, showing successful differentiation of malware classes.
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.