AI-Powered Network Management with Enhancing Reliability and Security

Nazar, Mustafa; Abbas Majeed, Adil; Gulmira Karimovna, Kudaiberdieva; Mohammed Jafar, Qusay; Khalil, Baker Mohammed

doi:10.22034/jipm.2025.728107

AI-Powered Network Management with Enhancing Reliability and Security

Document Type : Original Article

Authors

Mustafa Nazar ¹

Adil Abbas Majeed ²

Kudaiberdieva Gulmira Karimovna ³

Qusay Mohammed Jafar ⁴

Baker Mohammed Khalil ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728107

Abstract

Abstract
Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management.
Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost.
Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed.
Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection.
Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Keywords

Keywords: AI

network management

reliability

security

machine learning (ML)

deep learning (DL)

anomaly detection

IoT

predictive maintenance

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