IoT Traffic Anomaly Detection Using Weight-Shared BERT
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Solution Overview
Problem
Existing deep learning-based anomaly detection methods for IoT network traffic face challenges in resource consumption and lack of parallelism, particularly with BERT models, which are computationally intensive and hard to deploy on resource-constrained devices, while traditional methods struggle to effectively capture temporal and spatial dynamics in network traffic data.
Innovation Solution
An improved BERT model integrating contrastive learning is developed, utilizing weight sharing among Transformer encoder networks and incorporating a cross-entropy and contrastive loss function to enhance generalization and reduce resource consumption, while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If BERT model is applied for traffic anomaly detection, then detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The BERT model is segmented into two parts: a pre-trained base model and a task-specific classification head. Only the classification head is fine-tuned for traffic anomaly detection, while the base model parameters remain frozen. This segmentation reduces the number of trainable parameters and computational resources required during inference, while maintaining the detection accuracy benefits of BERT.
Solution Approach 2:
The model configuration is optimized by reducing the number of BERT layers from 12 to 6, and adjusting embedding dimensions and hidden layer sizes. These parameter changes reduce memory consumption and computational complexity while preserving the essential capability to capture long-range dependencies in network traffic data.
2Reliability
If full BERT model is used, then long-range dependency capture is improved, but device adaptability deteriorates
Solution Approach 1:
Instead of using the complete 12-layer BERT model, only 6 layers are deployed in the traffic anomaly detection system. This partial action approach provides sufficient capability to capture long-range dependencies in network traffic while reducing the model size and resource requirements to levels suitable for deployment on resource-constrained IoT devices.
3Reliability
If conventional defense measures are deployed on IoT devices, then security capability is improved, but device performance deteriorates
Solution Approach 1:
Traditional mechanical defense measures such as antivirus software and firewalls are replaced with a lightweight deep learning model. This substitution enables anomaly detection with lower computational overhead and memory consumption, maintaining security capability while preserving device performance on resource-constrained IoT platforms.
Data Source
AI summary
A traffic anomaly detection method and system based on improved BERT integrating contrastive learning is provided, the method includes: obtaining traffic data and preprocessing the data; building an improved BERT model including an embedding layer and 12 Transformer encoder networks; performing weight sharing operation among a first 6 Transformer encoder networks and a last 6 Transformer encoder networks, respectively; building a classification network; building a total loss function based on a cross-entropy loss and a contrastive loss; performing unsupervised pre-training on the improved Bidirectional Encoder Representations from Transformers, BERT model; fine-tune training the improved BERT model; updating model parameters through backpropagation to obtain a trained improved BERT model; feed test traffic data into the trained improved BERT model; and obtain a traffic detection outcome. The present disclosure enhances the generalization ability of the model while maintaining stability and accuracy.