The increasing sophistication of cyber threats, particularly zero-day attacks, poses a significant challenge to conventional security mechanisms, which primarily rely on signature-based or heuristic detection methods. These traditional approaches are often incapable of identifying novel or obfuscated threats due to their dependency on known attack signatures or predefined rules. This research proposes an experimental framework leveraging deep learning and behavior-based modeling to enhance zero-day attack detection capabilities in dynamic computing environments. By capturing system and user behavioral patterns through enriched telemetry data and training advanced neural architectures such as LSTM and autoencoders, the proposed model learns to recognize deviations indicative of malicious activity. Experimental evaluation was conducted using a curated dataset containing both benign and malicious behaviors, including emulated zero-day scenarios. The results demonstrate a significant improvement in detection accuracy, achieving over 92% precision and reduced false positives compared to conventional intrusion detection systems. Furthermore, the model exhibits adaptive learning characteristics, enabling it to detect previously unseen attacks without explicit retraining. This study underscores the potential of integrating behavioral analytics with deep learning to construct resilient, intelligent cybersecurity systems. The findings contribute to the growing domain of AI-driven cyber defense and open avenues for real-time, autonomous threat mitigation strategies.

Keywords: Zero-day detection, behavior-based cybersecurity, deep learning, anomaly detection, LSTM, cyber threat intelligence