Exploiting the Power of Machine Learning for Proactive Anomaly Detection and Threat Mitigation in the Burgeoning Landscape of Internet of Things (IoT) Networks
Keywords:
Internet of Things (IoT), Anomaly Detection, Machine LearningAbstract
IoT growth is unmatched owing to networked devices in every aspect of our lives and businesses. Innovation benefits from ubiquitousness, yet it has downsides. Many IoT devices attract unscrupulous individuals who exploit flaws to cause mayhem. Unchecked data breaches, privacy infractions, and crucial infrastructure outages may occur. The study explores machine learning (ML) as a powerful defense against these threats.
ML models for anomaly detection in dynamic IoT networks are carefully selected. We assess the pros and cons of supervised, unsupervised, and mixed learning. Supervised learning on labeled normal and aberrant behaviour datasets may provide impressive results. Getting enough tagged data for IoT situations is difficult. IoT networks contain more unlabeled data for unsupervised learning. However, their inability to identify irregularities requires vigilance. Combining approaches is exciting but needs careful planning and integration.
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