4 citations · 4 across the 4 of their papers we have counts for
6 papers
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios
Haohan Shi, Xiyu Shi, Safak Dogan +3
Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and chann…
A Novel Byte-Level Flow-to-Image Encoding Method for Network Intrusion Detection Systems
Ziyu Mu, Zihui Yan, Xiyu Shi +1
Network-based Intrusion Detection Systems (IDS) are predominantly trained on tabular flow records, whose one-dimensional representations limit convolutional architectures from expl…
GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance
Ziyu Mu, Xiyu Shi, Safak Dogan
Intrusion Detection System (IDS) is often calibrated to known attacks and generalizes poorly to unknown threats. This paper proposes GMA-SAWGAN-GP, a novel generative augmentation…
A Novel Solution for Zero-Day Attack Detection in IDS using Self-Attention and Jensen-Shannon Divergence in WGAN-GP
Ziyu Mu, Xiyu Shi, Safak Dogan
The increasing sophistication of cyber threats, especially zero-day attacks, poses a significant challenge to cybersecurity. Zero-day attacks exploit unknown vulnerabilities, makin…
Audio Deepfake Detection at the First Greeting: "Hi!"
Haohan Shi, Xiyu Shi, Safak Dogan +2
This paper focuses on audio deepfake detection under real-world communication degradations, with an emphasis on ultra-short inputs (0.5-2.0s), targeting the capability to detect sy…
Multi-Granularity Adaptive Time-Frequency Attention Framework for Audio Deepfake Detection under Real-World Communication Degradations
Haohan Shi, Xiyu Shi, Safak Dogan +2
The rise of highly convincing synthetic speech poses a growing threat to audio communications. Although existing Audio Deepfake Detection (ADD) methods have demonstrated good perfo…