Cyber Security Using Machine Learning Approaches: A Systematic Review
Mohan K. N. Kumar, Pavan E. Kumar · Apple Academic Press eBooks · 2024
Machine learning in cyber security has recently made headlines. With the widespread increase of population, the need for reliable mechanism to prevent cyberattacks has increased in manifold. In the recent days, there is an increase in cyber security problems in majority of the enterprises across the globe. The reason for cyberattacks problems is not specific but it has become so uncertain. If we take a sample from the population, it should not be a surprise to see a enterprise suffered from cyberattacks irrespective of the technology adopted; for example Ransomware, Spyware, and Trojans are commonly found. So, this situation poses a serious challenge for researchers to find the root cause. It is difficult to accurately predict the future cyberattacks based on the current status because the scenario might not be the same for all the enterprises. Providing an affordable, high quality Cyber security service has become a big challenge. In this sense, cyberattack avoidance has been studied for decades, which is an area with 196 a steady stream of new work and improvement over time. This article is based on a systematic investigation that aims to bring together prior research on cyberattack prediction and categorization, highlight significant changes in patterns, and propose research direction for future work.