Ai-Powered Cyber Threat Intelligence System for Predicting and Preventing Cyber Attacks

Abstract

This paper aims at discussing and analyzing ways in which artificial intelligence revolutionizes the approach to cybersecurity by focusing on data. This work indicates the incorporating of AI in cybersecurity strategies not only improves security but also minimizes expenditures and errors, all needed in modern-world cybersecurity. The expansion of various fields and industries, along with the integration of numerous smart devices that are connected to the internet, has resulted in a highly secured threat level. Cybersecurity is mainly about identifying threats and responding to them, but that is not possible today with traditional methods. Modern threats and their constant evolution are partially beyond the capacity of traditional security instruments to protect an organization or company Combining anomaly detection and machine learning (ML) techniques enables the system to adapt to changing security threats. The first phases involve gathering and analyzing data from numerous cloud sources to improve the system's capacity to spot problems. Supervised learning with Random Forest classifies known hazards, while unsupervised learning with Isolation Forest detects new abnormalities. Real-time monitoring and response considerably improve the system's threat detection rates (95%), anomaly detection (93%), and other performance indicators. The proposed system surpasses the existing system by 95% accuracy, 93% precision, and 96% recall. These findings demonstrate how effectively the framework enables cloud safety and its capacity to enhance overall digital safety and proactively prevent assaults.