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.
Our approach combines multiple audio analysis techniques including:
- Audio Feature Extraction: Advanced signal processing to extract relevant features from voice communications
- Large Audio Model Integration: Leveraging state-of-the-art audio models for pattern recognition
- Real-time Detection: Implementation of efficient algorithms for live voice analysis
- Adversarial Robustness: Techniques to handle sophisticated audio manipulation attempts
The framework demonstrates significant improvements in detection accuracy compared to existing methods, achieving high precision and recall rates while maintaining low false positive rates. Our research contributes to the growing body of work on AI-enabled cybersecurity and provides practical solutions for protecting against voice-based social engineering attacks.
Key Contributions
- Novel Audio Analysis Framework: Development of a comprehensive system for voice phishing detection
- Large Model Integration: Successful application of large audio models to security applications
- Real-time Capabilities: Implementation of efficient detection algorithms for live analysis
- Adversarial Defense: Robustness against sophisticated audio manipulation techniques
Research Impact
This work advances the field of audio-based cybersecurity and provides practical tools for organizations to protect against voice phishing attacks. The framework can be integrated into existing security infrastructure to provide real-time protection against vishing threats.