Research on Machine Learning-Based Threat Detection and Security Defense Systems for Telecom Operator Networks

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Published: 2026-01-16
Keywords:
machine learning, threat detection, security defense systems, telecom networks, intrusion detection, anomaly detection, cybersecurity

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Logan Kendrick
Department of Computer Science, University of North Texas, Denton, USA

Abstract

Telecom operator networks face increasing and evolving threats, necessitating advanced security defense systems. Traditional security mechanisms often struggle to keep pace with sophisticated cyberattacks. Machine learning (ML) offers promising solutions for proactive threat detection and automated security responses. This review paper surveys the research on machine learning-based threat detection and security defense systems specifically designed for telecom operator networks. We begin by providing a historical overview of security challenges and the evolution of defense mechanisms in telecom networks. Subsequently, we delve into two core themes: (A) ML-based intrusion detection systems (IDS) focusing on anomaly detection and signature-based techniques, and (B) ML-driven security defense, including automated threat mitigation and adaptive security policies. We compare different ML algorithms применяются in these themes, analyze their performance metrics, and identify the challenges associated with their deployment in real-world telecom environments, with a focus on data privacy and computational complexity. Finally, we explore future research directions, highlighting the potential of federated learning, explainable AI (XAI), and reinforcement learning to enhance the resilience and security of telecom operator networks against emerging cyber threats. This review aims to provide a comprehensive understanding of the current state-of-the-art and future trends in this critical area.

Article Details

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Vol. 2 No. 1 (2026)

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Articles

How to Cite

Research on Machine Learning-Based Threat Detection and Security Defense Systems for Telecom Operator Networks. (2026). Hua Xia Xin Zhi, 2(1), 9-19. https://journals.hubblepress.com/index.php/hxxz/article/view/15

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