A recent research on malware detection using machine learning algorithm: Current challenges and future works

Abstract

Each year, malware issues remain one of the cybersecurity concerns since malware’s complexity is constantly changing as the innovation rapidly grows. As a result, malware attacks have affected everyday life from various mediums and ways. Therefore, a machine learning algorithm is one of the essential solutions in the security of computer systems to detect malware regarding the ability of machine learning algorithms to keep up with the evolution of malware. This paper is devoted to reviewing the most up-to-date research works from 2017 to 2021 on malware detection where machine learning algorithm including K-Means, Decision Tree, Meta-Heuristic, Naïve Bayes, Neuro-fuzzy, Bayesian, Gaussian, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and n-Grams was discovered using a systematic literature review. This paper aims at the following: (1) it describes each machine learning algorithm, (2) for each algorithm; it shows the performance of malware detection, and (3) we present the challenges and limitations of the algorithm during research processes.