Machine Learning and Deep Learning Approaches for Cyber Security

Abstract

Cyber security mainly prevents the hardware, software, and data present in the system that has an active internet connection from external attacks. Organizations mainly deploy cyber security for their databases and systems to prevent it from unauthorized access. This survey paper describes a Cyber security hiring and retention challenges are bigger than ever this year. View the State of Cyber security 2022 infographic to see the cyber workforce challenges and opportunities faced by enterprises around the world--and to see how your organization compares. The objective of this research work is to present the evaluation of some of the widely used machine learning techniques used to detect some of the most threatening cyber threats to the cyberspace. Three primary machine learning techniques are mainly investigated, including deep belief network, decision tree and support vector machine. We have presented a brief exploration to gauge the performance of these machine learning techniques in the spam detection, intrusion detection and malware detection starting from IP traffic classification, filtering malicious traffic for intrusion detection based on frequently used and benchmark datasets. Various attacks have been classified using the ML algorithms and finally the performance of each algorithm have been assessed. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.