Beyond Semantic Noise: Diagnosing and Correcting Structural Bias in Code-Mixed Script Detection via XAI-Driven Hybridization

In the contemporary cybersecurity landscape, the detection of code-mixed malicious scripts embedded within high-trust domains (e.g., governmental and academic websites) constitutes a critical defensive challenge. Traditional Transformer-based models, while effective in natural language processing, often exhibit "Structural Bias," where they erroneously interpret the benign complexity of legacy HTML structures as malicious obfuscation, resulting in elevated false positive rates. To address this limitation, this study proposes an XAI-Driven Hybrid Architecture that synergizes context-aware semantic embeddings from WangChanBERTa with outlier-robust structural features. Validated on a rigorously curated high-fidelity corpus of 5,000 samples, our model achieves a state-of-the-art F1-Score of 0.9908. Beyond standard metrics, Explainable AI (XAI) diagnosis reveals a critical "Dual-Validation" mechanism: structural features effectively veto semantic hallucinations triggered by benign complexity, acting as a crucial safety net. Crucially, the proposed architecture functions as a 'Dual-Validation' mechanism, where structural features effectively veto semantic hallucinations triggered by benign complexity. The integration of these components leads to a 50% reduction in the False Positive Rate (FPR), decreasing from 0.024 in baseline scenarios to 0.012, thereby confirming the operational significance of Selective Integration. This method effectively reduces 'alert fatigue,' providing a scalable solution for SOC analysts tasked with protecting critical infrastructure from advanced code-mixed threats.