Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures

Abstract

The alarming security threats in the internet world continually raise critical concerns among individuals, organizations and governments alike. The sophistication of cyber-attacks makes it imperative for a paradigm shift from traditional approaches and measures for quelling the attacks to modern sophisticated, digital and strategic ones, such as those involving machine learning and other technologies of artificial intelligence (AI). This study is aimed at examining machine learning (ML) strategies for effective cyber security. ML involves using algorithms and statistical models to enable computers learn from and make decisions or predictions based on data. The study relied on secondary data, which were subjected to a systematic review. The results of its thematic and qualitative analyses prove that majority of the literatures allude to the fact that the maximal performance abilities and tactics of the ML constitute its strategies for quelling cyber security. These include its: early detection of threats that are tackled before they cause damages; ability to analyze huge quantity of data quickly and accurately; and processing of datasets in real-time. The study argues that the noted abilities and tactics constitute ML strategies for quelling cyber security, regardless of its challenges like data quality, security vulnerabilities and possible incidences of bias. The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.