Increasing the Predictive Potential of Machine Learning Models for Enhancing Cybersecurity

Abstract

Networks have an increasing influence on our modern life, making Cybersecurity an important field of research. Cybersecurity techniques mainly focus on antivirus software, firewalls and intrusion detection systems (IDSs), etc. These techniques protect networks from both internal and external attacks. This research is composed of three different essays. It highlights and improves the applications of machine learning techniques in the Cybersecurity domain. Since the feature size and observations of the cyber incident data are increasing with the growth of internet usage, conventional defense strategies against cyberattacks are getting invalid most of the time. On the other hand, the applications of machine learning tasks are getting better consistently to prevent cyber risks in a timely manner. For the last decade, machine learning and Cybersecurity have converged to enhance risk elimination. Since the cyber domain knowledge and adopting machine learning techniques do not align on the same page in the case of deployment of data-driven intelligent systems, there are inconsistencies where it is needed to bridge the gap. We have studied the most recent research works in this field and documented the most common issues regarding the implementation of machine learning algorithms in Cybersecurity. According to these findings, we have conducted research and experiments to improve the quality of service and security strength by discovering new approaches.