Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition.

Area of Science:

• Computer Science
• Cybersecurity
• Artificial Intelligence

Background:

• Malware poses a significant and growing threat to computer security, with millions of new samples daily.
• Traditional machine learning methods struggle with the increasing sophistication and volume of malware.
• Effective malware classification is crucial for security teams to prioritize threats.

Purpose of the Study:

• To propose a novel deep learning model for accurate malware classification.
• To categorize malware families and perform multiclassification efficiently.
• To address the limitations of traditional methods in handling advanced malware.

Main Methods:

• A deep learning model, specifically a convolutional neural network (CNN), was developed.
• Malware files were converted into grayscale images for visual analysis.
• The CNN was trained and evaluated on a dataset of 10,000 Microsoft malware samples across nine categories.

Main Results:

• The proposed deep learning model achieved a high accuracy of 99.97% for classifying nine distinct malware types.
• The method demonstrated superior performance compared to existing deep learning models.
• The image-based classification approach proved effective for malware identification.

Conclusions:

• The novel deep learning model offers a highly accurate and efficient solution for malware classification.
• Converting malware files to images for CNN analysis is a promising technique in cybersecurity.
• This approach can aid security teams in effectively managing the escalating threat of malware.