42 citations · 106 across the 20 of their papers we have counts for
22 papers
LogGPT: Log Anomaly Detection via GPT
Xiao Han, Shuhan Yuan, Mohamed Trabelsi
Detecting system anomalies based on log data is important for ensuring the security and reliability of computer systems. Recently, deep learning models have been widely used for lo…
Robust Fraud Detection via Supervised Contrastive Learning
Vinay M. S., Shuhan Yuan, Xintao Wu
Deep learning models have recently become popular for detecting malicious user activity sessions in computing platforms. In many real-world scenarios, only a few labeled malicious…
Achieving Counterfactual Fairness for Anomaly Detection
Xiao Han, Lu Zhang, Yongkai Wu +1
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detec…
On Root Cause Localization and Anomaly Mitigation through Causal Inference
Xiao Han, Lu Zhang, Yongkai Wu +1
Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and ac…
Generating Textual Adversaries with Minimal Perturbation
Xingyi Zhao, Lu Zhang, Depeng Xu +1
Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidat…
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors
Yik-Cheung Tam, Jiacheng Xu, Jiakai Zou +3
Performance of spoken language understanding (SLU) can be degraded with automatic speech recognition (ASR) errors. We propose a novel approach to improve SLU robustness by randomly…
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations
He Cheng, Depeng Xu, Shuhan Yuan +1
Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data. A…
Trustworthy Anomaly Detection: A Survey
Shuhan Yuan, Xintao Wu
Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models…
LogBERT: Log Anomaly Detection via BERT
Haixuan Guo, Shuhan Yuan, Xintao Wu
Detecting anomalous events in online computer systems is crucial to protect the systems from malicious attacks or malfunctions. System logs, which record detailed information of co…
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
Shuhan Yuan, Xintao Wu
Insider threats, as one type of the most challenging threats in cyberspace, usually cause significant loss to organizations. While the problem of insider threat detection has been…
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
Che Wang, Shuhan Yuan, Kai Shao +1
A simple and natural algorithm for reinforcement learning (RL) is Monte Carlo Exploring Starts (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and t…
Identifying Hidden Buyers in Darknet Markets via Dirichlet Hawkes Process
Panpan Zheng, Shuhan Yuan, Xintao Wu +1
The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit tr…
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
Depeng Xu, Shuhan Yuan, Xintao Wu
Preserving differential privacy has been well studied under centralized setting. However, it's very challenging to preserve differential privacy under multiparty setting, especiall…
Insider Threat Detection via Hierarchical Neural Temporal Point Processes
Shuhan Yuan, Panpan Zheng, Xintao Wu +1
Insiders usually cause significant losses to organizations and are hard to detect. Currently, various approaches have been proposed to achieve insider threat detection based on ana…
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Panpan Zheng, Shuhan Yuan, Xintao Wu
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulen…
FairGAN: Fairness-aware Generative Adversarial Networks
Depeng Xu, Shuhan Yuan, Lu Zhang +1
Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predict…
One-Class Adversarial Nets for Fraud Detection
Panpan Zheng, Shuhan Yuan, Xintao Wu +2
Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipe…
Spectrum-based deep neural networks for fraud detection
Shuhan Yuan, Xintao Wu, Jun Li +1
In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spe…
Wikipedia Vandal Early Detection: from User Behavior to User Embedding
Shuhan Yuan, Panpan Zheng, Xintao Wu +1
Wikipedia is the largest online encyclopedia that allows anyone to edit articles. In this paper, we propose the use of deep learning to detect vandals based on their edit history.…
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Shuhan Yuan, Xintao Wu, Yang Xiang
Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries…
SNE: Signed Network Embedding
Shuhan Yuan, Xintao Wu, Yang Xiang
Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal…
On Spectral Analysis of Directed Signed Graphs
Yuemeng Li, Xintao Wu, Aidong Lu
It has been shown that the adjacency eigenspace of a network contains key information of its underlying structure. However, there has been no study on spectral analysis of the adja…