no code implementations • 15 Feb 2024 • He Cheng, Shuhan Yuan
In this paper, we explore compromising deep sequential anomaly detection models by proposing a novel backdoor attack strategy.
no code implementations • 28 Sep 2023 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications.
1 code implementation • 25 Sep 2023 • Xiao Han, Shuhan Yuan, Mohamed Trabelsi
However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection.
no code implementations • 19 Aug 2023 • Vinay M. S., Shuhan Yuan, Xintao Wu
In many real-world scenarios, only a few labeled malicious and a large amount of normal sessions are available.
1 code implementation • 4 Mar 2023 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings.
1 code implementation • 8 Dec 2022 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal.
1 code implementation • 12 Nov 2022 • Xingyi Zhao, Lu Zhang, Depeng Xu, Shuhan Yuan
Many word-level adversarial attack approaches for textual data have been proposed in recent studies.
1 code implementation • 8 Nov 2022 • Yik-Cheung Tam, Jiacheng Xu, Jiakai Zou, Zecheng Wang, Tinglong Liao, Shuhan Yuan
Knowledge cluster classification is boosted from 0. 7924 to 0. 9333 in Recall@1.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 9 Oct 2022 • He Cheng, Depeng Xu, Shuhan Yuan, Xintao Wu
Given a sequence that is detected as anomalous, we can consider anomalous entry detection as an interpretable machine learning task because identifying anomalous entries in the sequence is to provide an interpretation to the detection result.
no code implementations • 15 Feb 2022 • Shuhan Yuan, Xintao Wu
Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection.
no code implementations • 25 May 2020 • Shuhan Yuan, Xintao Wu
We then discuss such challenges and suggest future research directions that have the potential to address challenges and further boost the performance of deep learning for insider threat detection.
no code implementations • ICLR 2022 • Che Wang, Shuhan Yuan, Kai Shao, Keith Ross
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 the policy is improved by choosing actions that maximize the current estimate of the Q-function.
no code implementations • 12 Nov 2019 • Panpan Zheng, Shuhan Yuan, Xintao Wu, Yubao Wu
The key challenge is that the buyers are anonymized in darknet markets.
no code implementations • 11 Nov 2019 • Depeng Xu, Shuhan Yuan, Xintao Wu
Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.
3 code implementations • 12 Sep 2018 • Panpan Zheng, Shuhan Yuan, Xintao Wu
However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform.
no code implementations • 28 May 2018 • Depeng Xu, Shuhan Yuan, Lu Zhang, Xintao Wu
In this paper, we focus on fair data generation that ensures the generated data is discrimination free.
1 code implementation • 5 Mar 2018 • Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.
1 code implementation • 3 Jun 2017 • Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang
In particular, we develop a multi-source long-short term memory network (M-LSTM) to model user behaviors by using a variety of user edit aspects as inputs, including the history of edit reversion information, edit page titles and categories.
no code implementations • 3 Jun 2017 • Shuhan Yuan, Xintao Wu, Yang Xiang
The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.
no code implementations • 3 Jun 2017 • Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible.