AI Enhanced Cyber Triggered Threat Detection and Prevention using Deep Learning

Abstract

The complexity and regularity of cyberthreats have made the adoption of proactive and intelligent defence mechanisms necessary in the ever-changing field of cybersecurity. This paper presents an AI-enhanced framework for cyber threat detection and prevention applying methods from deep learning. The suggested system makes use of recurrent neural networks (RNNs), convolutional neural networks (CNNs), and autoencoders to look at network traffic, system logs, and behavioural patterns in real-time, enabling accurate threat identification with minimal false positives. The model incorporates anomaly detection, automated incident classification, and adaptive response strategies that improve over time via reinforcement learning. Furthermore, the system facilitates rapid recovery from attacks by isolating affected components and restoring data using predictive backup management. Experimental results demonstrate improved threat response times, increased detection accuracy, and robust recovery from both known and novel cyberattacks. This approach aims to move cybersecurity from a reactive to a proactive model, enhancing the resilience and autonomy of digital infrastructures.