Cyber Security Tools and Techniques suggested for mitigating insider attacks in the healthcare system

Cybersecurity plays a vital role today. Due to the increase in digitalization, numerous threats are possible nowadays. Many fields, like education, the military, the banking sector, etc., need to protect a huge amount of data. The health sector is one of the major fields where the patient’s data must be secured in a protective way. All the threats and vulnerabilities in the health sector must be identified, and the researchers must give more importance to mitigating these threats. It also focused on the insider attack, which is considered a harmful attack for the organization. The protection of the data can be achieved through cybersecurity tools. This research paper briefly identifies the cybersecurity tools and techniques that help to mitigate and provide more support to protect our data. This review paper addresses the tools, techniques, challenges, and state of the art of cybersecurity. Also, it addresses the collaboration with Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques to detect and automate insider attacks in the health sector. After the analysis of tools in the different fields, the implementation was started with the Support Vector Machine, the Decision tree algorithm, Random Forest, AdaBoost, and Naive Bayes.