A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage
arXiv:2606.21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat. We present a hybrid pipeline that scores each modality with its own engine and fuses the results. Three engines are built, deployed, and independently benchmarked: a four-stage URL stack (Domain Guard, lexical model, threat intelligence, and an asymmetric L2 fusion sidecar); a generalization-hardened DistilBERT NLP classifier whose held-out real-phishing recall rises from 0.8% to 87.3%; and a threat-intelligence synchronizer with end-to-end OpenTelemetry instrumentation confirming 1:1 message conservati
The increasing sophistication of multi-modal cyber threats necessitates more robust and hybrid defense mechanisms, driving innovation in AI-powered phishing and threat classification.
This development significantly enhances the ability to detect and neutralize advanced phishing attacks, protecting individuals, organizations, and critical infrastructure from financial and data loss.
Phishing detection is no longer solely reliant on single-modality analysis, but instead leverages a fused, multi-channel approach that incorporates independent validation and improved NLP engines.
- · Cybersecurity companies
- · Organizations with high-value data
- · Individuals susceptible to phishing
- · AI/ML researchers
- · Cybercriminals
- · Traditional, single-modality detection systems
Reduced success rates for sophisticated phishing campaigns.
Increased trust in digital communication channels for sensitive interactions.
Potential for an 'arms race' where attackers develop even more sophisticated evasion techniques.
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Read at arXiv cs.CL