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May 7, 20260 citationsOpen Access

Architecture of an Intelligent Security Monitoring System for IoT Devices in a Protected Digital Educational Environment

DBDinmukhammed BagdatKazakh University of Technology and Business

Key Points

The study aims to develop an intelligent security monitoring system for IoT devices in educational settings.
Utilized Isolation Forest and One-Class SVM models on a Raspberry Pi 4.
Enabled local processing of network traffic to enhance data privacy.
Focused on edge-oriented architecture for efficient anomaly detection.
Achieved a detection accuracy of 0.962.
Successfully identified network anomalies with minimal data privacy concerns.

Abstract

This paper presents an edge-oriented intelligent security monitoring system for IoT devices within digital educational environments. Using Isolation Forest and One-Class SVM models on a Raspberry Pi 4, the system detects network anomalies with high accuracy (0.962) while ensuring data privacy by processing traffic locally

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