Open Access

ARTICLE

FICNet: A Deep Learning Framework for Intrusion Detection in Agricultural Internet of Things

Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*

1 Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
2 Centre for Image and Vision Computing (CIVC), Centre of Excellence for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Selangor, Malaysia
3 Berlin School of Business & Innovation (BSBI), Berlin, Germany
4 Human and Digital Interface Department, JW Kim College of Future Studies, Woosong University, Daejeon, Republic of Korea
5 AI and Big Data Department, Woosong University, Daejeon, Republic of Korea

* Corresponding Authors: Muhammad Iqbal Hossain. Email: ; Sarina Mansor. Email:

Computer Modeling in Engineering & Sciences 2026, 148(1), 48 https://doi.org/10.32604/cmes.2026.081254

Received 26 February 2026; Accepted 12 May 2026; Issue published 27 July 2026

Abstract

The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves 8% data efficiency advantage over traditional baselines. Interpretability analysis via Integrated Gradients identifies header size, byte counts, and directional ratios as primary discriminative features. Comparative evaluation against 15 baselines including Transformer and GNN architectures validates FICNet’s effectiveness with only 147,092 parameters (1.81 MB), demonstrating suitability for resource-constrained agricultural IoT deployments.

---

Keywords

---