Machine Learning Based Context-Aware Security Management for Mobile IoT: A New Adaptive Approach to Threat Mitigation
DOI:
https://doi.org/10.54228/mjaret0624023Keywords:
Mobile IoT; Machine Learning; Context-Aware Security; Adaptive Threat Mitigation; Anomaly Detection; Real-Time Security; IoT Threat ManagementAbstract
As mobile devices are connected to the Internet of Things (IoT), potentially dynamic and distributed environments are difficult to keep safe. Due to an increase in context-specific cyber-attacks, conventional security mechanisms fail to tackle 30% of the threats. This paper proposes a novel context-aware security management framework for self-adaptive and self-healing mobile IoT environments. The framework uses machine learning models to detect normal and anomalous behavior patterns, ensuring real-time alerting and mitigation of threats. The framework uses adaptive machine learning techniques to understand new threats, minimizing false positives (45%) and enhancing overall security performances. With the help of extensive simulations, we find that our framework shows a 40% lower number of security breaches, and 50 more accurate detection of threats, making it suitable for next-generation mobile IoT networks.
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