Adaptive Cloud–Edge Coordination for Real-Time Phishing URL Detection With Distributed Caching and ONNX-Based Inference
Dam Minh Linh, Duy-Hung Ha, Trong Thua Huynh, Công Hùng Trần, Miroslav Vozn̆ák · IEEE Access · 2026
Phishing detection systems continue to struggle with real-time responsiveness in distributed web environments. This paper proposes a cloud–edge coordination framework that integrates browser-side Uniform Resource Locator (URL) interception, adaptive cache coordination (ACC), and open ONNX-accelerated inference. The lightweight hybrid model (DistilBERT–MLP fusion) fuses DistilBERT embeddings with standardized numerical features through a multilayer perceptron (MLP). Experiments demonstrate a detection accuracy above 99%, millisecond-level neural inference latency, and end-to-end detection times below 150 ms in live browser environments. ACC reduces cold-cache initialization from multisecond delays to microsecond-level cache lookups. Before ACC, a first-time blacklist fetch takes 7–8 s, and subsequent lookups complete in under 1 μs, with total detection latency ranging between approximately 2 and 110 ms. This behavior demonstrates the effectiveness of ACC in reducing repeated network queries. The experimental results substantiate three principal contributions: an efficient hybrid lexical–numeric detector, an ACC-driven mechanism that mitigates cold-start delays, and a cloud–edge design that strengthens real-time phishing protection at the web scale.