Search Results for author: Jiqiang Liu

Found 11 papers, 0 papers with code

CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications

no code implementations20 Jan 2024 Hangsheng Zhang, Jiqiang Liu, Jinsong Dong

Ensemble defenses, are widely employed in various security-related applications to enhance model performance and robustness.

Adversarial Robustness

TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph

no code implementations6 Apr 2023 Nan Wang, Xuezhi Wen, Dalin Zhang, Xibin Zhao, Jiahui Ma, Mengxia Luo, Sen Nie, Shi Wu, Jiqiang Liu

APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT).

DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks

no code implementations14 Oct 2021 Yixiang Wang, Jiqiang Liu, Xiaolin Chang, Jianhua Wang, Ricardo J. Rodríguez

In this paper, we propose an interpretable white-box AE attack approach, DI-AA, which explores the application of the interpretable approach of the deep Taylor decomposition in the selection of the most contributing features and adopts the Lagrangian relaxation optimization of the logit output and L_p norm to further decrease the perturbation.

AppQ: Warm-starting App Recommendation Based on View Graphs

no code implementations8 Sep 2021 Dan Su, Jiqiang Liu, Sencun Zhu, Xiaoyang Wang, Wei Wang, Xiangliang Zhang

In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.

Recommendation Systems

IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks

no code implementations3 Feb 2021 Yixiang Wang, Jiqiang Liu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić

To further make the perturbations more imperceptible, we propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly, which can restrict the total perturbations and perturbation points simultaneously.

DNN Testing

Generalizing Adversarial Examples by AdaBelief Optimizer

no code implementations25 Jan 2021 Yixiang Wang, Jiqiang Liu, Xiaolin Chang

Recent research has proved that deep neural networks (DNNs) are vulnerable to adversarial examples, the legitimate input added with imperceptible and well-designed perturbations can fool DNNs easily in the testing stage.

Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

no code implementations9 May 2019 Di Zhao, Jiqiang Liu, Jialin Wang, Wenjia Niu, Endong Tong, Tong Chen, Gang Li

"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.

Attribute Clustering

The NiuTrans Machine Translation System for WMT18

no code implementations WS 2018 Qiang Wang, Bei Li, Jiqiang Liu, Bojian Jiang, Zheyang Zhang, Yinqiao Li, Ye Lin, Tong Xiao, Jingbo Zhu

This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks.

Machine Translation Translation

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

no code implementations18 Jul 2018 Tong Chen, Wenjia Niu, Yingxiao Xiang, Xiaoxuan Bai, Jiqiang Liu, Zhen Han, Gang Li

In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples.

CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

no code implementations7 Jul 2018 Lun Li, Jiqiang Liu, Lichen Cheng, Shuo Qiu, Wei Wang, Xiangliang Zhang, and Zonghua Zhang

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems.

Privacy Preserving

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