Shattering Weak Facades: Trustworthy Detection of Encrypted Malicious Traffic via Uncertainty-Aware Fusion

作者：Meihui Zhong, Chengtai Cao, Wenxin Tai, Fan Zhou · 发表于：IEEE Transactions on Dependable and Secure Computing · 年份：2026 · DOI：10.1109/tdsc.2026.3667905 · 研究领域：Internet Traffic Analysis and Secure E-voting、Security and Verification in Computing、Network Security and Intrusion Detection

Graph Neural Networks (GNNs) are promising for Encrypted Malicious Traffic Detection (EMTD), yet practical deployments often face weak information: broken graph structures, incomplete node features, and scarce training data. Prior methods partially mitigate these issues but still suffer from (i) restricted information propagation range in graph structures, (ii) imprecise graph structure reconstruction resulting in erroneous or missing connections, and (iii) absence of a unified framework to address multiple facets of information sparsity jointly. In response, we propose TrustWI, an uncertainty-aware multi-view framework. It builds three complementary views to recover and enrich signal under weak information: (i) long-range propagation to expand information flow, (ii) post-propagation structural augmentation to repair broken connections, and (iii) view interaction modeling to capture cross-view synergy. We further develop an evidential, uncertainty-aware fusion that quantifies prediction uncertainty at both the view level and the global level, yielding robust decisions. Extensive evaluations across three benchmarks validate the effectiveness of TrustWI, demonstrating substantial improvements in accuracy and trustworthiness under weak information conditions. Notably, our approach advances the state-of-the-art AUC from 85.45% to 89.57% in extreme information-constrained scenarios.