Authors

Taylor, Onate Egerton

Department of Computer Science, Rivers State University, Port Harcourt, Nigeria

Odemenem, Ndidi Patience

Department of Computer Science, Rivers State University, Port Harcourt, Nigeria

Abstract

The widespread proliferation of Internet of Things (IoT) devices created significant security and privacy vulnerabilities due to their resource-constrained nature and the inadequacy of traditional cybersecurity frameworks. This study addressed the pressing need for a secure, intelligent, and real-time remote monitoring system tailored for IoT environments. The proposed solution integrated a lightweight anomaly detection module utilizing machine learning algorithms, which was designed for efficient deployment on edge nodes (e.g., Raspberry Pi, ESP32) to minimize latency and enhance scalability. An encryption mechanism, such as AES-128, ensured secure and authenticated data exchange, overcoming the limitations of conventional protocols. A comprehensive web-based dashboard, built with Python Flask and React.js, provided real-time threat visualization, alerts, and user control functionalities, enhancing administrative decision-making. Empirical evaluation demonstrated the system's efficacy, achieving a detection accuracy of 99.2% with Decision Trees, inference latency under 7 ms, encryption/decryption overhead of ~0.09 ms, and end-to-end alert latency of 21.04 ms, with CPU utilization at 85.12% on edge nodes. This research contributed a practical, scalable, and resilient cybersecurity framework that significantly improved the integrity, confidentiality, and availability of IoT networks, fostering greater trustworthiness in critical IoT applications.

Keywords

IoT Security Edge Computing Anomaly Detection Machine Learning Real-Time Monitoring Data Encryption

Citation of this Article

Taylor, Onate Egerton, & Odemenem, Ndidi Patience. (2025). Secure Remote Monitoring System against Cyber Threats in IoT Network. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(11), 1-7. Article DOI: https://doi.org/10.47001/JAIET/2025.211001

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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