BGA: A noise-immune neural distillation framework for malicious signature extraction in high-entropy encrypted flows
To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA, a noise-immune neural distillation framework for encrypted threat intelligence.The methodology first employs Analysis of Variance (ANOVA) to decouple high-discriminatory control-plane features - specifically industrial setpoints - from stochastic cryptographic noise. To resolve the extreme class imbalance within a corpus of 86,878 flow records, a Wasserstein GAN with Gradient Penalty (WGAN-GP) module, enforcing the 1-Lipschitz constraint, is integrated to synthesize high-fidelity minority samples, elevating the detection recall of rare Malicious State Command Injections(MSCI) attacks by 43.2%. At its core, the B...
| Authors | Sheng Hong, Yixuan Huang, Weiwei Jiang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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