A Comparative Study of Machine Learning Algorithms for Malware Analysis

Abstract

Comparing various machine learning algorithms on malware analysis is the process of evaluating the performance of different algorithms by using a dataset of labeled malware samples. The process includes training multiple models using algorithms such as XG-Boost, Random Forest, Naive Bayes, and k-NN and comparing their performance using various metrics like precision-recall, accuracy, and F1-score. The best algorithm for a given problem will rely upon the characteristics of the dataset and the requirements of the application. This process can help to develop an algorithm suitable for a specific problem and dataset to optimize the overall performance of the malware detection system.