Cyber Threat Intelligence (CTI): An Analysis on the Use of Artificial Intelligence and Machine Learning to Identify Cyber Hazards

Cyber Threat Intelligence (CTI): An Analysis on the Use of Artificial Intelligence and Machine Learning to Identify Cyber Hazards

연구 분야: Safety

학회: International Conference on Recent Developments in Cyber Security

Data transport volume and scope on networks are growing daily due to the quick advancements in network technology. It is challenging for cybersecurity specialists to keep track of every action taking place on the network because of the constantly growing density of networks. This circumstance has led to an increase in the complexity and intensity of threats and attacks. It is harder to detect and identify irregularities in network activities because of frequent and sophisticated cyberattacks. A well-crafted cybersecurity strategy now includes cyber threat intelligence (CTI), which is a crucial foundation. Automating the detection of cyberattacks as well as speedy attack type analysis and predication are all made possible by machine learning (ML), which offers a number of tools and techniques. The strategies for using machine learning (ML) to identify assaults are discussed in this article. Threat intelligence can help security teams defend against a constantly evolving threat environment before, during, and after an attack if used properly. By analyzing attackers and comprehending their tactics and goals, groups may create cyber defenses that are more effective, delicate, and resilient. However, due to two significant flaws, its usefulness is still in question. First, current methods are unable to detect unknown Indicator of Compromise (IoC), and second, they are unable to automatically produce categorized CTIs which renders CTI sharing. As a result, the objective of this paper is to present a complete analysis of cyber threat identification using intelligent techniques. Additionally, we covered the issues and solutions related to machine learning applications utilized in network assaults.