🤖 AI Summary
This study addresses the need for trustworthy cybersecurity assessment in high-risk scenarios within open-source ecosystems by proposing a shallow, interpretable hybrid architecture that integrates neural networks, Bayesian inference, and symbolic logic. The approach embeds domain knowledge and causal reasoning through differentiable components, augmented with a residual attention mechanism and expert-defined risk amplifiers—such as blast radius and propagation velocity—to emulate deep reasoning while preserving structural transparency. Innovatively, five epistemic axioms—precision, causality, falsifiability, transparency, and completeness—are hard-constrained into the gating mechanism to guarantee intrinsic interpretability. Evaluated on 20 open-source projects covering all OWASP Top 10:2025 categories and multilingual vulnerabilities, the model achieves confidence scores of 0.79–0.97 and outperforms conventional black-box models in accuracy.
📝 Abstract
We introduce the Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), a hybrid neural architecture for explainable cybersecurity risk assessment in open-source ecosystems. Unlike deep models that trade interpretability for accuracy, our shallow network encodes domain knowledge, causal reasoning, and expert judgment as differentiable components. It uses 80 interpretable neurons across 12 layers, including a gatekeeper that enforces five epistemological axioms - precision, causality, falsifiability, transparency, and completeness - as hard constraints before propagation. Despite limited depth, the network exhibits deep-learning traits via residual attention and feedback loops, learning complex risk patterns without becoming a black box. It produces fully decomposable scores: a deterministic weighted component plus an expert adjustment, with each adjustment traceable to named amplifiers (blast radius, propagation speed, structural nature, default exposure, exploitation pattern, institutional criticality). We validate on 20 open-source projects covering all OWASP Top 10:2025 categories and language risk classes, achieving confidence scores of 0.79-0.97, and show that explainability is guaranteed by design, not by a training algorithm. This challenges the assumption that deep learning requires deep networks, proving that shallow networks with deep reasoning can outperform opaque models in high-stakes cybersecurity, where interpretability is essential.