Development of an AI-Enhanced Intrusion Detection System for Detecting Zero-Day Attacks in Enterprise Networks

• Author(s): Emmanuel Udeme Edet; Dr. Nelson Ogbogu
• Paper ID: 1718202
• Page: 4830-4833
• Published Date: 29-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Zero-day attacks pose a significant threat to modern enterprise networks because they can exploit previously unknown vulnerabilities before security patches or signatures are available. Traditional intrusion detection systems (IDS), which rely primarily on signature-based detection, are inadequate for identifying such emerging threats. This paper presents the development of an AI-enhanced hybrid intrusion detection system designed to improve the detection of zero-day attacks in enterprise environments. The proposed system integrates machine learning and deep learning techniques within a hybrid framework that combines anomaly-based and misuse-based detection mechanisms. Network traffic data are subjected to preprocessing operations including feature extraction, normalization, and dimensionality reduction before classification using supervised and unsupervised learning models. Experimental evaluation demonstrates that the proposed IDS achieves higher detection accuracy and lower false positive rates compared to conventional IDS approaches. The results confirm that artificial intelligence significantly enhances enterprise security by enabling adaptive and real-time threat detection. This study contributes a practical and scalable framework for deploying intelligent intrusion detection systems capable of responding effectively to evolving cyber threats.

Keywords

Intrusion Detection System, Zero-Day Attacks, Artificial Intelligence, Machine Learning, Deep Learning, Enterprise Networks, Cybersecurity.

Citations

IRE Journals:
Emmanuel Udeme Edet, Dr. Nelson Ogbogu "Development of an AI-Enhanced Intrusion Detection System for Detecting Zero-Day Attacks in Enterprise Networks" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 4830-4833 https://doi.org/10.64388/IREV9I11-1718202

IEEE:
Emmanuel Udeme Edet, Dr. Nelson Ogbogu "Development of an AI-Enhanced Intrusion Detection System for Detecting Zero-Day Attacks in Enterprise Networks" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718202