AI-DRIVEN THREAT DETECTION: ENHANCING CYBERSECURITY WITH DEEP LEARNING

Abstract

With the increasing complexity and volume of cyber threats, traditional security mechanisms are becoming inadequate in detecting sophisticated attacks. Artificial Intelligence (AI), particularly Deep Learning (DL), has emerged as a powerful tool in cybersecurity for identifying and mitigating threats in real time. This paper explores AI-driven threat detection techniques, leveraging deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders to enhance cybersecurity. The study examines real-world cybersecurity datasets, highlighting the effectiveness of deep learning algorithms in anomaly detection, malware classification, and intrusion detection systems (IDS). Experimental results demonstrate that AI-powered threat detection systems significantly outperform conventional approaches in terms of accuracy, adaptability, and threat prediction capabilities. The findings underscore the potential of deep learning in building robust and proactive cybersecurity defenses.