Artificial Intelligence for Threat Detection: Leveraging Deep Learning to Identify Zero-Day Attacks in Real Time

Citation: Geol Kang, "Artificial Intelligence for Threat Detection: Leveraging Deep Learning to Identify Zero-Day Attacks in Real Time", Universal Library of Engineering Technology, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article examines the use of deep artificial intelligence models to detect zero-day attacks amid rapidly escalating cyber-threat complexity. The relevance stems from the fact that the emergence rate of zero-day exploits consistently outpaces the rate of signature generation. At the same time, traffic encryption, malware polymorphism, and the growth of attack automation markedly degrade the efficacy of classical IDS. The objective is to conduct a systematic analysis of deep learning’s potential to provide real-time detection of unknown vulnerabilities and to assess the architectures, training regimes, and telemetry requirements that determine a model’s ability to recognize previously unobserved patterns. The study’s novelty lies in a multi-level methodological integration: mapping modern neural architectures to the characteristics of enterprise traffic, analyzing model robustness to concept drift and adversarial interference, and evaluating prospects for autonomous vulnerability remediation and the use of post-quantum cryptographic mechanisms in distributed learning. The main findings underscore that deep learning transforms the threat-detection paradigm: it shifts defense from retrospective signatures to behavioral baselines, compresses dwell time from days to seconds, and enables a reactive-proactive security loop. The effectiveness of such systems is determined not only by architectural choice, but also by the maturity of telemetry collection, verification, and versioning processes; resilience to data poisoning; decision interpretability; and engineering optimization of computational pipelines. The work also identifies critical constraints: scarcity of labeled traces, behavioral drift, hardware costs, and the risk of model compromise. These limits delineate development directions, from generating temporary patches with large language models to offloading inference to edge devices and employing digital twins for continuous self-training of detection systems. The article will be of use to researchers, security engineers, and SOC professionals deploying behavioral mechanisms for zero-day attack detection.

Keywords: Artificial Intelligence, Deep Learning, Zero-Day Attacks, Behavioral Detection, Cybersecurity.

https://doi.org/10.70315/uloap.ulete.2025.0204013