Leveraging AI-enabled framework in Cybersecurity middleware platforms for Real-time risk analysis and attack prevention

Abstract

The increasing complexity and rate of cyberattacks have become a major problem to standard security measures, where sophisticated tools are needed that can process risk and prevent attacks in real-time. This paper introduces RAAP-AI (Real-time AI-based Attack Prevention to Risk Analysis) which is a new framework that employs the use of artificial intelligence in the implementation of real-time security monitoring, threat detection, and real-time response solutions. This is motivated by the fact that current security systems cannot effectively handle the large amount of security information in real-time and are prone to high false positives. RAAP-AI is a multi-layered architecture based on deep learning, ensemble techniques, and risk-based scoring that can maximize detection accuracy and reduce response latency. The approach combines various sources of data, uses sophisticated preprocessing tools and a hybrid AI engine to identify the threat and risk levels. The contributions are significant as the detection accuracy is high, 92 percent decrease in false positives over current methods, and sub-second reaction to serious threats. In the results of the experiment, RAAP-AI has surpassed numerous metrics such as precision (97.3%), recall (96.8%), and F1-score (97.0) and established its potential to become a transformative solution to the next-generation cybersecurity infrastructure.