Enhancing Cyber Threat Intelligence and Security Automation: A Comprehensive Approach for Effective Protection

Network management gets more difficult as technology develops, and Internet information is more readily available. As a result, cyberattacks have increased as well, making it harder for organizations to identify and stop threats. It has become essential to create a cyber threat intelligence team. In order to improve threat identification and response using cyber threat intelligence, this project will make use of artificial intelligence and machine learning. In order to increase the precision and speed of threat detection, it investigates powerful techniques including supervised and unsupervised learning. The report recommends investing in cutting-edge technology, knowledgeable personnel, and encouraging advancement to address issues of combining CTI and security automation. It evaluates the effects of security automation and CTI on incident response, effectiveness, cost savings, and compliance. The study uses machine learning approaches to recognize threat actors and classify them according to the nature of the assault and their organizational affiliation. The accuracy of the KNN model was 93%, whereas the accuracy of the logistic regression was 97.5%. The paper uses the same technique as the research article “Threat Actor Type Inference and Characterization within Cyber Threat Intelligence,” which it builds upon.