Applications and Challenges of Machine Learning (ML) in Cyber Security

Machine learning (ML) approaches have found widespread application in various scientific domains due to their unique attributes, including scalability, versatility, and the capacity to promptly regulate to novel and unfamiliar challenges. With the tremendous advancements in online banking, social networks, cloud and web technologies, mobile environments, smart grids, and other areas, cybersecurity is a rapidly growing problem that requires a lot of attention. Several machine learning algorithms have proven effective in addressing a wide range of computer security concerns. The various uses of machine learning in cybersecurity are considered with an emphasis on them. Cyberthreat identification and security, AI-based software for virus detection, behavior modeling, fighting AI-based threats, network intrusion detection, monitoring of emails, malware detection and classification, watering hole, remote exploitation, and security concerns with machine learning algorithms itself are few of them.