Intrusion Detection Using Machine Learning in Internet of Things Networks

. S. V. Raghavan

Intrusion Detection Using Machine Learning in Internet of Things Networks

Author(s)	Prof. . S. V. Raghavan
Country	United States
Abstract	The rapid expansion of Internet of Things (IoT) networks has introduced significant opportunities for intelligent automation, real-time monitoring, and interconnected digital services across industrial, healthcare, smart city, and consumer environments. However, the increasing number of connected devices has also expanded the cybersecurity attack surface, creating new challenges related to unauthorized access, malicious activities, data manipulation, and network intrusions. Traditional intrusion detection approaches often struggle to manage the complexity, scalability, and dynamic behaviour of modern IoT environments. This study investigates the application of machine learning techniques for intrusion detection in IoT networks by developing a simulation-based analytical framework. The research evaluates how machine learning models can improve threat identification, anomaly detection, and network security performance through intelligent pattern recognition. The proposed framework considers important evaluation parameters, including detection accuracy, precision, recall, and F1-score, to assess intrusion detection effectiveness. The analytical findings indicate that machine learning-based intrusion detection approaches provide improved capability for identifying complex attack patterns compared with conventional security mechanisms. However, challenges related to data imbalance, computational limitations, privacy protection, and model adaptability remain significant concerns. The study contributes to the understanding of intelligent cybersecurity approaches for IoT ecosystems and highlights the importance of adaptive machine learning frameworks for developing secure and resilient connected networks.
Keywords	Intrusion Detection System; Machine Learning; Internet of Things; Cybersecurity; Anomaly Detection; Network Security; Artificial Intelligence.
Field	Engineering
Published In	Volume 1, Issue 4, July-August 2019
Published On	2019-08-02

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American International Journal of Research and Innovation

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Intrusion Detection Using Machine Learning in Internet of Things Networks

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