A Comprehensive Machine Learning Framework for Securing Mobile Edge Computing Against New Threats
DOI:
https://doi.org/10.54228/mjaret0624022Keywords:
Mobile Edge Computing (MEC); Machine Learning; Multilayer Security; Anomaly Detection; Cybersecurity; Intrusion Prevention; Threat DetectionAbstract
Mobile edge computing (MEC) allows significant reductions in data latency as servers are located closer to end-users, but also raises serious security concerns due to its decentralized design. This paper presents a novel MEC security framework consisting of a layered security architecture scope and applying a machine learning-driven approach to improve the network and physical security of the mobile edge. Through neural network-based models, the framework predicts real-time anomalies, mitigates intrusion attempts, and adapts to dynamic threats. The proposed solution shows a 45% increased fine-grained threat detection rate as well as a 30% reduction in system latency to support real-time applications, that achieve high-security demands. The presented framework combats cyber-attacks to secure the mobile edge and ensures data integrity. It demonstrates a robust security infrastructure to address the rapidly evolving security landscape of MEC.
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