Automatically Detecting Voice Phishing: A Large Audio Model Approach
Abstract Voice phishing (vishing) attacks represent a growing threat in cybersecurity, with attackers increasingly using sophisticated audio manipulation techniques to deceive victims. This paper presents a novel approach to automatically detecting voice phishing attacks using large audio models. We develop a comprehensive framework that leverages advanced audio processing techniques and machine learning to identify malicious voice communications in real-time.
Jan 1, 2026
Automatic Extraction of Protected Health Information from Multilingual Hacker Communities
A multi-task relation learning approach for extracting protected health information from multilingual hacker communities.
Jan 1, 2026
A Domain-Adaptive Soft Prompting Framework for Multi-Type Bias Detection in News
A domain-adaptive soft prompting framework for multi-type bias detection in news content.
Jan 1, 2026
Large Language Models for Cybersecurity
Pioneering research on the application of large language models for cybersecurity text analysis, threat detection, and security intelligence.
Jan 1, 2024
Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach
A deep transfer learning approach for creating proactive cyber threat intelligence using hacker exploit labels.
Jan 1, 2024
Mapping Exploit Code on Paste Sites to the MITRE ATT&CK Framework: A Multi-label Transformer Approach
A multi-label transformer approach for mapping exploit code to the MITRE ATT&CK framework.
Jan 1, 2023
Disrupting Ransomware Actors on the Bitcoin Blockchain: A Graph Embedding Approach
A graph embedding approach for disrupting ransomware actors on the Bitcoin blockchain.
Jan 1, 2023