Alignment

The main problem addressed in this research is the apparent literary gap between good exploration of machine learning and the various algorithms used and their role in enhancing cybersecurity. Therefore, the purpose of this study is to explore the literary world to identify such associations to suggest possible new ways of improving cybersecurity. The primary purpose of cybersecurity is safeguarding the systems and networks from attacks and unauthorized access of information. Cybersecurity uses various mechanisms and techniques to detect and resolve attacks. Security professionals examine available research and literature for multiple techniques and implement them in their organizations to safeguard systems. The study seeks to provide literature on the various machine learning algorithms and the extent of application against cyber threats (Aravindan et al., 2020).

The research questions are:

1. Are machine learning algorithms for cyber threats effective, and do they efficiently execute their role of protection?
2. What are the ethical considerations in using machine learning algorithms in combating cyber-attacks?
3. Are there any cyber threats that use machine learning algorithms as part of their attack portfolio?
4. Are there any demerits and pertinent issues related to using machine learning algorithms in mitigating cyber-attacks?

The hypothesis:

The null hypothesis (H0) is: There exists no significant association between the implementation of machine learning algorithms against cyber threats and improved security.

The alternative hypothesis (H1): There exists a significant association between the implementation of machine learning algorithms against cyber threats and improved security.