Machine Learning Techniques for Cyber Security: A Review

Cyber security crimes continue to increase every day. As the devices and the network connectivity is increasing, attackers and hackers committing the crimes over these diversely connected devices are also increasing. This brings a major attention to stop these attacks, and the focus has been moved to machine learning cyber threats, but with its advancement, it has now been used in multiple different ways to reduce the cyber-attacks. Although the non-availability of proper dataset is one of the limitations which were there in most of the studies. This paper will give the extensive details about the research done to understand the ML models and to use for those in preventing cyber-attacks models to protect the devices from the three major domains of attacks, namely spam, malware, and intrusions attacks. This paper will present the review of the studies which have been done previously to reduce the cyber-attacks using machine learning. It will discuss the implementation of the commonly used models used to predict the intrusions, malware, and spam detection, followed by which there will be a comparative analysis of these models for the three domains of the cyber-attacks. This review will also discuss the limitations and the future work to enhance the security of the network devices.