Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning

2025-01-25

AI Summary

To address the challenge of high-accuracy, real-time intrusion detection in Industrial Internet of Things (IIoT) environments characterized by multi-class, highly imbalanced data, this work proposes a lightweight autoencoder-decision tree hybrid model—first deployed on the Jetson Nano edge device. Methodologically, an unsupervised autoencoder performs feature dimensionality reduction and reconstruction to mitigate class imbalance, while an optimized decision tree enables efficient classification. Model pruning, post-training quantization, and edge-specific deployment optimizations ensure low-latency inference under stringent resource constraints. Evaluated on the Edge-IIoTset benchmark, the model achieves 99.94% accuracy and F1-score, with average binary- and multi-class inference latencies of only 0.185 ms and 0.187 ms, respectively—significantly outperforming existing edge-based intrusion detection systems.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened the risk of cyberattacks, necessitating robust security measures to protect IIoT networks. Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities. Despite the potential of Machine Learning (ML)--based IDS solutions, existing models often face challenges with class imbalance and multiclass IIoT datasets, resulting in reduced detection accuracy. This research directly addresses these challenges by implementing six innovative approaches to enhance IDS performance, including leveraging an autoencoder for dimensional reduction, which improves feature learning and overall detection accuracy. Our proposed Decision Tree model achieved an exceptional F1 score and accuracy of 99.94% on the Edge-IIoTset dataset. Furthermore, we prioritized lightweight model design, ensuring deployability on resource-constrained edge devices. Notably, we are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification. These results highlight the novelty and robustness of our approach, offering a practical and efficient solution to the challenges posed by imbalanced and multiclass IIoT datasets, thereby enhancing the detection and prevention of network intrusions.

Problem

• Industrial Internet of Things (IIoT)
• Intrusion Detection System (IDS)
• Accuracy and Efficiency

Innovation

• Autoencoder
• Decision Tree
• Industrial Internet of Things (IIoT) IDS