Machine Learning Techniques for Detecting Anomalies in IoT Networks
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Abstract
In addressing the imperative need for robust cybersecurity within smart home ecosystems, this research innovatively advocates for the integration of an advanced machine learning (ML)-based anomaly detection system, specifically tailored for the intricate web of IoT networks. Contemporary systems grapple with the complex challenge of efficiently pinpointing anomalies amidst vast, multifaceted streams of IoT data, a task growing ever more daunting with the exponential surge in connected devices. This monumental task necessitates groundbreaking strategies in energy prediction, task optimization, and real-time data processing. The study harnesses the power of the Random Forest algorithm, an ML technique renowned for its exceptional accuracy rates reaching up to 95.5%. The algorithm stands resilient even under strenuous conditions, such as environments rife with increased noise or burgeoning device volumes, proving its adaptability and reliability.The research's findings are profound, indicating a potential revolution in IoT security measures. The proposed system promises enhancements in detection precision, potentially increasing by 15-20%, alongside notable reductions in energy requirements by approximately 20%. Furthermore, the system shows formidable potential in thwarting unauthorized data access and breaches, aiming for a substantial decrease in these incidents by 35%. These achievements mark significant strides in fortifying IoT security infrastructure, setting a new standard in anomaly detection. The study concludes with forward-looking insights, advocating for the amalgamation of deep learning protocols and adaptive models, to further refine the anomaly detection process. This holistic, more nuanced approach could exponentially boost the system's efficiency, ensuring a safer, more secure digital environment for the burgeoning world of IoT.
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