Search Results for author:
Found 12 papers, 2 papers with code
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning
We show that in realistic FL settings, state-of-the-art (SOTA) defenses struggle to perform well against backdoor attacks in FL.
Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware
This malicious traffic can induce congestion and reduce Tor's performance, while encouraging network administrators to block Tor traffic.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
AuditNet: A Conversational AI-based Security Assistant [DEMO]
In the age of information overload, professionals across various fields face the challenge of navigating vast amounts of documentation and ever-evolving standards.
USAT: A Universal Speaker-Adaptive Text-to-Speech Approach
To prevent catastrophic forgetting and reduce storage implications for few-shot speaker adaptation, we designed two adapters and a unique adaptation procedure.
Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations
However, most current approaches suffer from the degradation of naturalness and speaker similarity when synthesizing speech for unseen speakers (i. e., speakers not in the training dataset) due to the poor generalizability of the model in out-of-distribution data.
AutoLV: Automatic Lecture Video Generator
We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video.
PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool
This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them.
Fake News Quick Detection on Dynamic Heterogeneous Information Networks
The spread of fake news has caused great harm to society in recent years.
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection
Our paper compares the performances of six well-known, state-of-the-art AutoML frameworks on ten different phishing datasets to see whether AutoML-based models can outperform manually crafted machine learning models.
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization
By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments.
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios
We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs).
PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites
PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80. 04%.
