Selected work

Representative Papers

Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews

Mar 13, 2026

This study addresses the growing sophistication of ransomware, including its adversarial attacks on AI models and the scarcity of high-quality data, by conducting the first “review of reviews” based on the PRISMA framework to systematically synthesize research on AI-driven ransomware defense from 2020 to 2024. Focusing on the integration of static and dynamic analysis, anomaly detection, and pre-encryption early-warning mechanisms, the work proposes an AI-powered defense roadmap that bridges theoretical advances with practical implementation. The research validates the efficacy of hybrid AI models in enabling real-time response and scalable defense architectures, identifies critical challenges, and offers concrete recommendations and collaborative pathways for researchers, industry practitioners, and policymakers.

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Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

Feb 22, 2026

This work proposes an end-to-end trustworthy detection framework to address three major challenges in cybersecurity threat detection: large-scale data volume, high risk of feature leakage, and opaque model decisions. The framework uniquely integrates strategic sampling—preserving class distribution to enhance training efficiency—an automated data leakage prevention mechanism, and model-agnostic SHAP-based interpretability analysis. Experimental evaluation on the CIC-IDS2017 dataset demonstrates that the proposed approach significantly reduces computational overhead while maintaining high detection performance. Furthermore, it delivers actionable explanations for security analysts, thereby facilitating the practical deployment of trustworthy AI in Security Operations Centers (SOCs).

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Embedding based Encoding Scheme for Privacy Preserving Record Linkage

Nov 01, 2025

This paper addresses privacy-preserving record linkage (PPRL): the problem of accurately identifying record pairs across distributed databases that refer to the same entity, while provably protecting sensitive raw data. To this end, we propose a novel PPRL method based on *q*-gram embedding and binary encoding. Specifically, *q*-grams are first mapped into a low-dimensional semantic embedding space; subsequently, a learnable quantization strategy transforms these embeddings into compact binary codes, enabling efficient and cryptographically secure similarity computation. The approach significantly improves matching accuracy—particularly for short-string records—and enhances robustness against re-identification and inference attacks. Extensive experiments on multiple real-world datasets demonstrate an average 8.2% improvement in F1-score over state-of-the-art baselines, while satisfying rigorous formal privacy guarantees (e.g., differential privacy or cryptographic security, as instantiated).

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Recent publications

Latest Papers

Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews

Mar 13, 2026

This study addresses the growing sophistication of ransomware, including its adversarial attacks on AI models and the scarcity of high-quality data, by conducting the first “review of reviews” based on the PRISMA framework to systematically synthesize research on AI-driven ransomware defense from 2020 to 2024. Focusing on the integration of static and dynamic analysis, anomaly detection, and pre-encryption early-warning mechanisms, the work proposes an AI-powered defense roadmap that bridges theoretical advances with practical implementation. The research validates the efficacy of hybrid AI models in enabling real-time response and scalable defense architectures, identifies critical challenges, and offers concrete recommendations and collaborative pathways for researchers, industry practitioners, and policymakers.

0 citationsRead paper

Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

Feb 22, 2026

This work proposes an end-to-end trustworthy detection framework to address three major challenges in cybersecurity threat detection: large-scale data volume, high risk of feature leakage, and opaque model decisions. The framework uniquely integrates strategic sampling—preserving class distribution to enhance training efficiency—an automated data leakage prevention mechanism, and model-agnostic SHAP-based interpretability analysis. Experimental evaluation on the CIC-IDS2017 dataset demonstrates that the proposed approach significantly reduces computational overhead while maintaining high detection performance. Furthermore, it delivers actionable explanations for security analysts, thereby facilitating the practical deployment of trustworthy AI in Security Operations Centers (SOCs).

0 citationsRead paper

Embedding based Encoding Scheme for Privacy Preserving Record Linkage

Nov 01, 2025

This paper addresses privacy-preserving record linkage (PPRL): the problem of accurately identifying record pairs across distributed databases that refer to the same entity, while provably protecting sensitive raw data. To this end, we propose a novel PPRL method based on *q*-gram embedding and binary encoding. Specifically, *q*-grams are first mapped into a low-dimensional semantic embedding space; subsequently, a learnable quantization strategy transforms these embeddings into compact binary codes, enabling efficient and cryptographically secure similarity computation. The approach significantly improves matching accuracy—particularly for short-string records—and enhances robustness against re-identification and inference attacks. Extensive experiments on multiple real-world datasets demonstrate an average 8.2% improvement in F1-score over state-of-the-art baselines, while satisfying rigorous formal privacy guarantees (e.g., differential privacy or cryptographic security, as instantiated).

0 citationsRead paper