2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 1 , PP: 126-134, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Reem Atassi 1 *
Doi: https://doi.org/10.54216/FPA.130110
The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation. However, this interconnected landscape also presents unique security challenges, necessitating robust intrusion detection mechanisms. In this research, we present a comprehensive study of anomaly detection in IoT networks, leveraging advanced machine learning techniques. Specifically, we employ the Gated Recurrent Unit (GRU) architecture as the backbone network to capture temporal dependencies within IoT traffic. Furthermore, our approach embraces hierarchical federated training to ensure scalability and privacy preservation across distributed IoT devices. Our experimental design encompasses public IoT datasets, facilitating rigorous evaluation of the model's performance and adaptability. Results indicate that our GRU-based model excels in identifying a spectrum of attacks, from Distributed Denial of Service (DDoS) incursions to SQL injection attempts. Visualizations of learning curves, Receiver Operating Characteristic (ROC) curves, and confusion matrices offer insights into the model's learning process, discriminatory power, and classification performance. Our findings contribute to the evolving landscape of IoT security, offering a roadmap for enhancing the resilience of interconnected systems in an era of increasing connectivity.
Internet of Things (IoT) , Anomaly Detection Algorithms , Intrusion Detection Systems , Machine Learning , Network Anomalies , Cybersecurity in IoT
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