An Ai-Based Framework for Intelligent Cyber Threat Detection and Prevention

Abstract

               Advanced security mechanisms that can detect and prevent cyber threats in real time are necessary due to their exponential growth. Conventional signature-based intrusion detection systems have trouble spotting new and complex attacks. Using machine learning and deep learning algorithms to examine network traffic patterns and spot unusual activity, this paper suggests an AI-based framework for intelligent cyber threat detection and prevention. The framework supports high-accuracy threat classification in complex network environments by integrating several AI models, such as ensemble methods, recurrent neural networks, and convolutional neural networks. Data collection, preprocessing, feature extraction, threat detection, and automated response modules comprise the multi-layered architecture of the suggested system.The framework is intended to demonstrate superior performance characteristics when applied to standard benchmark intrusion detection scenarios, enabling efficient identification of cyber threats like malware, DDoS attacks, and intrusion attempts. It is based on theoretical analysis and insights from existing literature. In order to provide interpretable threat analysis and help security analysts comprehend the reasoning behind detection, the framework also integrates explainable AI techniques. The development of intelligent cybersecurity systems that can adjust to changing threat landscapes while preserving operational effectiveness in corporate settings is aided by this research.