Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
This research introduces SMISH-GUARD, a multi-layer framework for adaptive SMS phishing (smishing) detection that integrates language-aware semantic modeling, graph-theoretic campaign reasoning, and cost-sensitive decision calibration within a unified architecture. The framework integrates dual transformer encoders for multilingual semantic understanding with a heterogeneous temporal graph layer that captures relational attack signals such as shared URLs, sender reuse, and campaign propagation patterns. A cost-sensitive decision optimization module is further incorporated to translate probabilistic model outputs into risk-aware alert policies that explicitly balance false-positive inconvenience against the higher societal and financial cost of missed smishing attacks. The study evaluates four integrated datasets comprising 51,528 SMS messages and 16 phishing attack subtypes under realistic class imbalance and multilingual conditions. The framework achieves PR-AUC of 0.943 and ROC-AUC of 0.950, while maintaining high recall for critical phishing subtypes. The ablation study confirms that each architectural component—including semantic modeling, graph-based risk propagation and decision calibration that contributes independently to overall robustness. Multilingual experiments also indicate promising generalization across linguistic contexts. The framework emphasizes deployment realism, incorporating probabilistic calibration, adversarial robustness considerations, and explainable decision thresholds suitable for adaptive on-device warning systems. The findings suggest that effective smishing defense requires not only accurate language models but also mathematically grounded decision policies and structurally aware representations of attacker behavior. SMISHGUARD establishes a principled foundation for next-generation smishing defense systems that combine semantic intelligence, relational reasoning, and human-centered risk optimization to mitigate evolving SMS threat.
Included in
GRP-07-163 SMISHGUARD: An AI-Powered Framework for SMS Phishing Detection and Alert System for Vulnerable Users
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
This research introduces SMISH-GUARD, a multi-layer framework for adaptive SMS phishing (smishing) detection that integrates language-aware semantic modeling, graph-theoretic campaign reasoning, and cost-sensitive decision calibration within a unified architecture. The framework integrates dual transformer encoders for multilingual semantic understanding with a heterogeneous temporal graph layer that captures relational attack signals such as shared URLs, sender reuse, and campaign propagation patterns. A cost-sensitive decision optimization module is further incorporated to translate probabilistic model outputs into risk-aware alert policies that explicitly balance false-positive inconvenience against the higher societal and financial cost of missed smishing attacks. The study evaluates four integrated datasets comprising 51,528 SMS messages and 16 phishing attack subtypes under realistic class imbalance and multilingual conditions. The framework achieves PR-AUC of 0.943 and ROC-AUC of 0.950, while maintaining high recall for critical phishing subtypes. The ablation study confirms that each architectural component—including semantic modeling, graph-based risk propagation and decision calibration that contributes independently to overall robustness. Multilingual experiments also indicate promising generalization across linguistic contexts. The framework emphasizes deployment realism, incorporating probabilistic calibration, adversarial robustness considerations, and explainable decision thresholds suitable for adaptive on-device warning systems. The findings suggest that effective smishing defense requires not only accurate language models but also mathematically grounded decision policies and structurally aware representations of attacker behavior. SMISHGUARD establishes a principled foundation for next-generation smishing defense systems that combine semantic intelligence, relational reasoning, and human-centered risk optimization to mitigate evolving SMS threat.