An Advanced Cybersecurity Framework for Malware Detection Using Byte N-Grams, Opcode Patterns, ASM Visualization, and XGBoost Classification

Abstract

The fast growth in the emergence of malware has resulted in the identification of malware detection as one of the vital problems in current cybersecurity issues. Signature-based approaches to malware detection have been proved to have numerous shortcomings in detecting polymorphic and metamorphic forms of malware. This paper describes the evaluation of an advanced cybersecurity solution, which utilizes several static analysis approaches – namely Byte N-Gram features extraction, Opcode sequences analysis, and Assembly (ASM) language representation – along with the XGBoost gradient boosting model. This approach is benchmarked against several well-known baseline models including Random Forest, Support Vector Machine (SVM), Naïve Bayes, and deep learning frameworks. The experiments were conducted on the Microsoft Malware Classification Challenge (BIG 2015) dataset, which consists of 10,868 examples of malware from nine different families. According to the results obtained from the experiments, the integrated framework shows the best classification performance of 98.6% accuracy, 98.3% precision, 97.9% recall, and 98.1% F1-score