(untitled) (International Journal of Engineering and Computer Science, 2025)
The escalating sophistication of cyber threats, including zero-day exploits, ransomware, and advanced persistent threats (APTs), has exposed the limitations of traditional cybersecurity solutions. These legacy systems often struggle to detect and mitigate rapidly evolving and adaptive attack vectors. In this context, artificial intelligence (AI), particularly deep learning, has emerged as a transformative technology capable of addressing these challenges. This paper explores the integration of deep learning techniques in real-time threat detection and prevention systems, focusing on their potential to enhance accuracy, speed, and adaptability. A systematic evaluation of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers, is conducted using widely recognized cybersecurity datasets, including CICIDS2017 and NSL-KDD. Metrics such as accuracy, precision, recall, F1-score, and latency are utilized to assess performance. The findings reveal that deep learning models significantly outperform traditional rule-based approaches by offering superior anomaly detection, faster response times, and enhanced capabilities to identify previously unknown threats. Key applications include intrusion detection systems, malware classification, and phishing attack prevention. Despite these advancements, challenges such as adversarial vulnerabilities, data quality limitations, and computational overhead remain critical barriers to adoption. Ethical concerns, including privacy risks and the transparency of AI decision-making processes, further complicate deployment. To address these issues, the study highlights the need for hybrid systems combining traditional techniques with AI-powered solutions, as well as the development of explainable AI (XAI) frameworks for increased trust and transparency. This research underscores the transformative potential of AI-driven deep learning in revolutionizing cybersecurity practices. By enabling proactive, real-time threat management, these technologies provide a robust foundation for mitigating cyber risks in an increasingly digital world. Future directions emphasize refining AI models, improving dataset quality, and advancing explainability to ensure responsible and effective implementation