Abstract

To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA , a noise-immune Neural Distillation neural distillation framework for encrypted threat intelligence. The methodology first employs Gated Residual Learning Analysis of Variance (ANOVA) to decouple high-discriminatory control-plane features High-Entropy Traffic specifically industrial setpointsfrom stochastic cryptographic noise. To resolve the extreme Generative Data Augmentation class imbalance within a corpus of 86,878 flow records , a Wasserstein GAN with Gradient Spatio-Temporal Modeling Penalty (WGAN-GP) module, enforcing the 1-Lipschitz constraint , is integrated to synthesize Industrial Edge Security high-fidelity minority samples, elevating the detection recall of rare Malicious State Command Injections(MSCI) attacks by 43.2% . At its core, the BGA architecture integrates Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency extraction and an Adaptive Gated Multi-Head Attention mechanism. This gated unit functions as a neural filter to dynamically suppress encryption artifacts while amplifying malicious signatures. Extensive evaluations on CIC-IDS-2018 and Edge-IIoT benchmarks demonstrate a performance ceiling exceeding 95.2% across all key metrics . Furthermore, noise-injection stress tests confirm BGAs superior structural resilience with a 8.57% performance margin over vanilla Transformers, while its ultra-low inference latency of 0.2820 ms (estimated 1.6920 ms via theoretical scaling for ARM) indicates a high potential for real-time feasibility on heterogeneous industrial edge gateways, providing a promising architectural baseline for future hardware implementation.