AI-Driven Anomaly Detection for Telecom Cloud Security
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-125Keywords:
AI-driven anomaly detection, Telecom cloud security, Machine learning (ML), Deep learning (DL), Network intrusion detection, Fraud detection, Performance degradation, Data privacy, Model interpretability, ScalabilityAbstract
Telecommunication networks are increasingly migrating to cloud-based infrastructures, introducing complexities that traditional security mechanisms struggle to address. AI-driven anomaly detection has emerged as a pivotal solution, leveraging machine learning (ML) and deep learning (DL) techniques to identify deviations from normal network behavior in real-time. This paper explores the integration of AI-based anomaly detection within telecom cloud environments, examining its effectiveness in enhancing security and operational efficiency. We analyze various AI methodologies, including supervised, unsupervised, and hybrid models, and their application in detecting network intrusions, performance degradation, and fraud. Additionally, we discuss the challenges associated with implementing AI solutions, such as data privacy concerns, model interpretability, and scalability. The paper concludes with recommendations for future research and development to optimize AI-driven anomaly detection systems in telecom cloud security
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