An Artificial Intelligence Based Predictive Model for Zero-Day Vulnerability Detection in Network Systems | IJCT Volume 12 – Issue 5 | IJCT-V12I5P71

Zero-day vulnerabilities are those sneaky software or network weaknesses that no one knows about yet meaning there are no patches available to fix them. They represent a major headache for modern cybersecurity. Cybercriminals are quick to take advantage of these vulnerabilities before anyone, including the vendors or defenders, even realizes they exist. This can lead to significant damage and long-lasting breaches. Traditional defense methods, which often depend on signature-based detection or vulnerability scanning, just can’t keep up with the speed and cleverness of these attackers, leaving organizations exposed to sudden and serious breaches. In this paper, we introduce an innovative AI-driven framework designed to predict zero-day vulnerabilities in network environments. Instead of just focusing on detecting active exploitation, our framework aims to foresee and prioritize potential weaknesses by analyzing data from multiple sources, configuration baselines, and contextual insights. It combines unsupervised anomaly detection, semi-supervised learning, and meta-learning for quick adaptation, all while ensuring that the insights are clear and actionable for security analysts. Through red-team simulation experiments and controlled evaluations using public datasets, our framework shows better recall and improved prioritization compared to traditional methods. We also delve into practical deployment, ethical considerations, and future research paths to make managing predictive zero-day vulnerabilities both practical and reliable.

This study introduced an innovative AI-driven framework designed to predict zero-day network vulnerabilities, which remain one of the toughest challenges in the world of cybersecurity. By using a hybrid strategy that combines anomaly detection, semi-supervised learning, and meta-learning, the framework showed impressive advancements compared to traditional detection methods. Tests conducted with public datasets, synthetic traffic, and red-team simulations validated the framework’s capability to identify new attack patterns with greater recall and precision, all while minimizing false positives. Adding contextual elements like threat intelligence and asset criticality further boosted the practical usefulness of the predictions, making sure that the results are actionable for security teams instead of just spitting out raw alerts. Beyond its technical achievements, the framework highlights the need for proactive and adaptable cybersecurity. While many current defense strategies are mostly reactive, this research demonstrates how predictive models can change the game by focusing on preemptive threat mitigation. By tackling both detection accuracy and operational prioritization, the framework offers a comprehensive solution that is not only technically sound but also relevant in real-world operations. In this way, it marks a significant advancement in helping organizations build stronger defenses against the increasing threats posed by zero-day vulnerabilities.

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