A COMPREHENSIVE REVIEW ON MACHINE LEARNING TECHNIQUES FOR THE IDENTIFICATION OF RANSOMWARE ATTACKS IN COMPUTER NETWORKS
Keywords:
Ransomware, Malware Attack, Learning Algorithms, Malware analysis, Internet resources.Abstract
ABSTRACT
Ransomware attacks have been identified as one of the serious threats in the cyber space. The malware poses serious security challenges to corporate networks and internet users worldwide. In response, several machine learning techniques have gained popularity for the classification of ransomware in the internet space when compared with signature-based approaches. This paper presented a comprehensive review of various studies that focus on the use of machine learning techniques for the identification of ransomware attacks in computer networks. The study collected relevant literature from various research databases by using some specific keywords and search strings that are deeply related to the topic. A good number of literatures that were obtained, were sorted and studied. The literatures were organised in different sections, arranged chronologically from the most recent to relatively older works. The publication years for the reviewed papers ranges from 2017 to 2023. The review began by exploring some relevant concepts and then shifted ground to machine learning algorithms that have been proposed for ransomware attacks identification. Thereafter, the performances of the different learning techniques used for the identification of ransomware attacks in computer networks were reported. The study argued that the review can serve as insights for future researches in this cyber security area.
