Citation

Thirumalaisamy, Manikandan and Yogarayan, Sumendra and Sayeed, Md Shohel and Abdul Razak, Siti Fatimah and Shunmugam, Ramesh (2025) Fog-Aware Hierarchical Autoencoder with Density-Based Clustering for AI-Driven Threat Detection in Smart Farming IoT Systems. Future Internet, 17 (12). p. 567. ISSN 1999-5903

Text Fog-Aware Hierarchical Autoencoder with Density-Based Clustering for AI-Driven Threat Detection in Smart FarmingIoT Systems.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstructionerror scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Jan 2026 02:29
Last Modified:	07 Jan 2026 08:17
URII:	http://shdl.mmu.edu.my/id/eprint/15161

Downloads

Edit (login required)