AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning

AI-driven cybersecurity has emerged as a transformative solution for combating increasingly sophisticated cyber threats. This research proposes an advanced deep learning-based cybersecurity framework aimed at enhancing threat detection and mitigation performance. Leveraging Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) architectures, the proposed model effectively identifies anomalies and classifies potential threats with high accuracy and minimal false positives. The framework was rigorously evaluated using real-time network traffic datasets, demonstrating a notable increase in detection accuracy by 18.5%, achieving a detection accuracy of 97.4%, compared to traditional machine learning methods (78.6%). Additionally, the response time to threats was significantly reduced by 25%, while computational overhead decreased by 30%, enhancing overall system responsiveness. Experimental results further show a 40% reduction in network downtime incidents due to faster identification and proactive mitigation of threats. The proposed AI-driven approach thus provides substantial improvements in security performance metrics, underscoring its potential for robust cybersecurity in dynamic and increasingly sophisticated threat landscapes