Research Article
AI-Driven Network Intrusion Detection Systems: A Systematic Review of Hybrid Models, Zero-Day Attack Mitigation, and Emerging Challenges in Cybersecurity
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 8 |
| Published: May 2025 |
| Authors: Sadiya Muhammad Rabiu, Bunyaminu Khalid Aminu, Dalhatu Aminu Zubairu |
10.5120/ijca2025925016
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Sadiya Muhammad Rabiu, Bunyaminu Khalid Aminu, Dalhatu Aminu Zubairu . AI-Driven Network Intrusion Detection Systems: A Systematic Review of Hybrid Models, Zero-Day Attack Mitigation, and Emerging Challenges in Cybersecurity. International Journal of Computer Applications. 187, 8 (May 2025), 27-33. DOI=10.5120/ijca2025925016
@article{ 10.5120/ijca2025925016,
author = { Sadiya Muhammad Rabiu,Bunyaminu Khalid Aminu,Dalhatu Aminu Zubairu },
title = { AI-Driven Network Intrusion Detection Systems: A Systematic Review of Hybrid Models, Zero-Day Attack Mitigation, and Emerging Challenges in Cybersecurity },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 8 },
pages = { 27-33 },
doi = { 10.5120/ijca2025925016 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Sadiya Muhammad Rabiu
%A Bunyaminu Khalid Aminu
%A Dalhatu Aminu Zubairu
%T AI-Driven Network Intrusion Detection Systems: A Systematic Review of Hybrid Models, Zero-Day Attack Mitigation, and Emerging Challenges in Cybersecurity%T
%J International Journal of Computer Applications
%V 187
%N 8
%P 27-33
%R 10.5120/ijca2025925016
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This systematic review synthesizes 45 peer-reviewed studies (2019–2024) on AI-driven Network Intrusion Detection Systems (NIDS) for enterprise cybersecurity. Advanced cyber threats, including zero-day exploits, adversarial AI, and ransomware, render traditional signature-based methods inadequate. AI-based NIDS, particularly hybrid models combining Machine Learning (ML) and Deep Learning (DL), exhibit superior detection accuracy, adaptability, and real-time responsiveness. Employing a PRISMA-guided methodology, this study evaluates hybrid ML-DL systems, zero-day detection techniques, adversarial countermeasures, and Explainable AI (XAI) frameworks. The meta-analysis indicates hybrid models achieve a mean accuracy of 96.2%, an F1-score of 0.94, and a 2.1% false positive rate, outperforming standalone ML (88.7% accuracy) and DL (92.5% accuracy) models by 10–15%. Real-world case studies in healthcare and smart cities, alongside cost-benefit analyses, demonstrate practical applicability. Standardized benchmarking protocols address dataset bias and adversarial vulnerabilities, validated in financial and healthcare sectors. The review proposes ethical AI frameworks, a future research roadmap, and deployment guidelines for enterprise Security Operations Centers (SOCs).
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