Dynamic-Aware Pruning-Quantization Compression for Browser-Based Anomaly Detection via WebAssembly Optimization

In recent years, anomaly detection in web applications has become increasingly important for security and reliability. For instance, detecting fraudulent transactions in e-commerce platforms or monitoring user behavior in real-time web dashboards requires immediate response and high accuracy. Traditional deep learning models are difficult to deploy in browsers due to CPU/GPU bottlenecks, limited memory, and stringent real-time latency requirements. Deploying deep learning models directly in the browser is challenging due to limited computational resources and memory constraints. To address these challenges, we propose DAPQ-AD, a Dynamic-Aware Pruning-Quantization compression algorithm optimized for WebAssembly execution. Unlike conventional static compression methods, DAPQ-AD not only dynamically adjusts pruning rates and quantization levels based on the characteristics of the input data, but it also adapts to varying input noise, preserves critical features for accurate anomaly detection, and reduces inference latency compared to static approaches. Extensive experiments demonstrate that our method significantly reduces model size and inference latency while maintaining high anomaly detection performance in browser environments. This approach enables lightweight, real-time anomaly detection directly on client-side web applications without compromising accuracy.