AI Driven Zero Day Vulnerability Detection and Exploit Prediction in Computer Networks | IJCSE Volume 9 – Issue 5 | IJCSE-V9I5P9

Zero-day vulnerabilities pose a major threat to today’s computer networks because they remain unknown and unpatched, allowing attackers to exploit systems before defenders detect the issue. Traditional security approaches based on signatures and rule sets often fail in such cases, as they cannot identify attacks that deviate from known patterns. This challenge is compounded by evolving tactics, polymorphic malware, and evasion methods designed to mimic normal behavior. Artificial intelligence (AI) and machine learning (ML) now offer promising solutions by analyzing massive, diverse data sources such as network logs, telemetry, and threat intelligence. Advanced models like deep learning, autoencoders, clustering, and explainable AI (XAI) enhance the detection of unusual activities and classification of new threats. Autoencoder-based frameworks reveal anomalies linked to unseen exploits, while ensemble and hybrid approaches enable anomaly detection and prediction using incomplete or unlabeled data. These AI-driven systems adapt continuously, learning from new data to update detection models and cut exploitation time. Modern tools like large language models (LLMs) and XAI agents can even assess complex software code and predict exploit likelihoods, reducing false positives and improving response prioritization. By adopting AI for zero-day detection and prediction, cybersecurity shifts from reactive defense to proactive risk management for critical infrastructure and enterprise systems.

This paper has presented a comprehensive examination of AI-driven zero-day vulnerability detection and exploit prediction in computer networks, highlighting the urgent need for innovative, proactive defense mechanisms against increasingly sophisticated cyber threats. Zero-day vulnerabilities remain a formidable challenge due to their unknown nature and the rapidity with which attackers exploit them. Traditional security approaches are inadequate, necessitating the adoption of intelligent systems capable of continuous learning and adaptive response. Reinforcement learning, combined with deep learning and natural language processing, offers a powerful framework for detecting zero-day exploits by modeling complex behavioral patterns and integrating contextual threat intelligence. Experimental results validate the efficacy of these AI techniques, demonstrating superior accuracy, scalability, and predictive capabilities compared to conventional methods. Moreover, the embedding of explainability mechanisms addresses critical trust and usability concerns, facilitating real-world deployment. The discussion underscored both the promise and challenges of AI adoption in cybersecurity, including issues related to false positives, adversarial AI threats, and the necessity for human oversight. Future directions emphasize federated learning, enhanced interpretability, and integration with automated security operations, which are expected to further enhance the responsiveness and resilience of network defenses. In conclusion, AI-driven zero-day vulnerability detection represents a vital evolution in cybersecurity, enabling organizations to shift from reactive to proactive defense postures. By leveraging adaptive machine learning models, enriched threat intelligence, and automated mitigation strategies, defenders can better anticipate, detect, and thwart unknown exploits. Continued research, development, and collaborative efforts will be essential to fully realize AI’s transformative impact on securing critical digital infrastructures against emerging cyber threats.

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