Deploying Isolation Forest at the Edge: A Synthetic Da-ta-driven Approach for Real-time IoT Anomaly Detection

• Author(s): Judah Idowu
• Paper ID: 1706999
• Page: 643-652
• Published Date: 30-01-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 7 January-2025

Abstract

This research addresses the challenge of detecting network anomalies in IoT environments by developing and evaluating AI-driven methodologies, specifically deploying Isolation Forest at the edge. A key obstacle in this domain is the scarcity of labeled datasets representing diverse anomaly types. To overcome this, we employed a synthetic data-driven approach, using statistical modeling to generate datasets that mimic real-world IoT scenarios, encompassing a wide range of normal and anomalous behaviors. This synthetic data enabled robust training and evaluation of our anomaly detection model. Performance was assessed using precision, recall, F1-score, and AUC-ROC. While the model demonstrated high preci-sion for normal data points, its performance in identifying anomalous data points was limited, as reflected in the low recall, F1-score, and ROC-AUC values. Despite these limitations, the study demonstrates the potential of Isolation Forest for detecting simpler anomalies like outliers and drifts, contributing to reduced false positives. Furthermore, the research emulated edge computing principles—distributed processing and reduced latency—to facilitate real-time analysis, which is crucial for mitigating security breaches and system failures in dynamic IoT environments. Although the simulation did not involve physical edge devices, it successfully demonstrated the feasibility of deploying Isolation Forest at the edge for real-time anomaly detection, highlighting its potential for enhancing operational efficiency, risk management, and security in sectors like healthcare, where early anomaly detection is critical.

Keywords

Artificial Intelligence, Edge Computing, IoT Security, Machine Learning, Statistical Methods

Citations

IRE Journals:
Judah Idowu "Deploying Isolation Forest at the Edge: A Synthetic Da-ta-driven Approach for Real-time IoT Anomaly Detection" Iconic Research And Engineering Journals Volume 8 Issue 7 2025 Page 643-652

IEEE:
Judah Idowu "Deploying Isolation Forest at the Edge: A Synthetic Da-ta-driven Approach for Real-time IoT Anomaly Detection" Iconic Research And Engineering Journals, 8(7)