ENHANCING CYBERSECURITY THROUGH MALWARE DETECTION BASED ON MACHINE LEARNING TECHNIQUE

Abstract

The world is now more connected through technology and this has given rise to cyber threats, and malware is one of the threats that a system and data need to guard against. In this paper, we propose a detailed framework wherein state of the art ML methodologies can be employed for malware categorization and identification. In the evaluation of the performances of various ML algorithms, we analyze Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Histogram-based Gradient Boosting (Hist GB), and AdaBoost. The algorithms are assessed in this study using an assembly command dataset and a static and dynamic analysis approach to improve the detection rate and stability. Out of all algorithms discussed, Random Forest, CatBoost, XGBoost, Hist GB are ranked highest with 99% accuracy. As for the accuracy, KNN yielded an accuracy of 97%. Performance analysis based on metrics shows that Random Forest, CatBoost, and Hist GB not only have high accuracy but also precision, recall, and F1-score. Particularly, the accuracy of Random Forest was 99% for both the precision, recall, and F1-score. These results confirm the use of ML-based solutions in the analysis and counteraction of modern malware threats and their advantages over traditional detection methods as well as strengthening cybersecurity