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321 results on '"Niu, Wenjia"'

1. Visual Prompting in LLMs for Enhancing Emotion Recognition

Author

Zhang, Qixuan, Wang, Zhifeng, Zhang, Dylan, Niu, Wenjia, Caldwell, Sabrina, Gedeon, Tom, Liu, Yang, and Qin, Zhenyue

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for emotion recognition in these models remains largely unexplored and untapped. Traditional methods in VLLMs struggle with spatial localization and often discard valuable global context. To address this problem, we propose a Set-of-Vision prompting (SoV) approach that enhances zero-shot emotion recognition by using spatial information, such as bounding boxes and facial landmarks, to mark targets precisely. SoV improves accuracy in face count and emotion categorization while preserving the enriched image context. Through a battery of experimentation and analysis of recent commercial or open-source VLLMs, we evaluate the SoV model's ability to comprehend facial expressions in natural environments. Our findings demonstrate the effectiveness of integrating spatial visual prompts into VLLMs for improving emotion recognition performance., Comment: Accepted by EMNLP2024 (Main, Long paper)

Published
2024

2. Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models

Author

Zhang, Ying, Zhou, Xiaoyan, Wen, Hui, Niu, Wenjia, Liu, Jiqiang, Wang, Haining, and Li, Qiang

Subjects
Computer Science - Cryptography and Security, Computer Science - Software Engineering
Abstract

Nowadays, the open-source software (OSS) ecosystem suffers from security threats of software supply chain (SSC) attacks. Interpreted OSS malware plays a vital role in SSC attacks, as criminals have an arsenal of attack vectors to deceive users into installing malware and executing malicious activities. In this paper, we introduce tactics, techniques, and procedures (TTPs) proposed by MITRE ATT\&CK into the interpreted malware analysis to characterize different phases of an attack lifecycle. Specifically, we propose GENTTP, a zero-shot approach to extracting a TTP of an interpreted malware package. GENTTP leverages large language models (LLMs) to automatically generate a TTP, where the input is a malicious package, and the output is a deceptive tactic and an execution tactic of attack vectors. To validate the effectiveness of GENTTP, we collect two datasets for evaluation: a dataset with ground truth labels and a large dataset in the wild. Experimental results show that GENTTP can generate TTPs with high accuracy and efficiency. To demonstrate GENTTP's benefits, we build an LLM-based Chatbot from 3,700+ PyPI malware's TTPs. We further conduct a quantitative analysis of malware's TTPs at a large scale. Our main findings include: (1) many OSS malicious packages share a relatively stable TTP, even with the increasing emergence of malware and attack campaigns, (2) a TTP reflects characteristics of a malware-based attack, and (3) an attacker's intent behind the malware is linked to a TTP., Comment: 19 pages, 11 figures

Published
2024

3. Obfuscating IoT Device Scanning Activity via Adversarial Example Generation

Author

Li, Haocong, Zhang, Yaxin, Cheng, Long, Niu, Wenjia, Wang, Haining, and Li, Qiang

Subjects
Computer Science - Cryptography and Security
Abstract

Nowadays, attackers target Internet of Things (IoT) devices for security exploitation, and search engines for devices and services compromise user privacy, including IP addresses, open ports, device types, vendors, and products.Typically, application banners are used to recognize IoT device profiles during network measurement and reconnaissance. In this paper, we propose a novel approach to obfuscating IoT device banners (BANADV) based on adversarial examples. The key idea is to explore the susceptibility of fingerprinting techniques to a slight perturbation of an IoT device banner. By modifying device banners, BANADV disrupts the collection of IoT device profiles. To validate the efficacy of BANADV, we conduct a set of experiments. Our evaluation results show that adversarial examples can spoof state-of-the-art fingerprinting techniques, including learning- and matching-based approaches. We further provide a detailed analysis of the weakness of learning-based/matching-based fingerprints to carefully crafted samples. Overall, the innovations of BANADV lie in three aspects: (1) it utilizes an IoT-related semantic space and a visual similarity space to locate available manipulating perturbations of IoT banners; (2) it achieves at least 80\% success rate for spoofing IoT scanning techniques; and (3) it is the first to utilize adversarial examples of IoT banners in network measurement and reconnaissance.

Published
2024

4. A Large-scale Fine-grained Analysis of Packages in Open-Source Software Ecosystems

Author

Zhou, Xiaoyan, Liang, Feiran, Xie, Zhaojie, Lan, Yang, Niu, Wenjia, Liu, Jiqiang, Wang, Haining, and Li, Qiang

Subjects
Computer Science - Software Engineering, Computer Science - Cryptography and Security
Abstract

Package managers such as NPM, Maven, and PyPI play a pivotal role in open-source software (OSS) ecosystems, streamlining the distribution and management of various freely available packages. The fine-grained details within software packages can unveil potential risks within existing OSS ecosystems, offering valuable insights for detecting malicious packages. In this study, we undertake a large-scale empirical analysis focusing on fine-grained information (FGI): the metadata, static, and dynamic functions. Specifically, we investigate the FGI usage across a diverse set of 50,000+ legitimate and 1,000+ malicious packages. Based on this diverse data collection, we conducted a comparative analysis between legitimate and malicious packages. Our findings reveal that (1) malicious packages have less metadata content and utilize fewer static and dynamic functions than legitimate ones; (2) malicious packages demonstrate a higher tendency to invoke HTTP/URL functions as opposed to other application services, such as FTP or SMTP; (3) FGI serves as a distinguishable indicator between legitimate and malicious packages; and (4) one dimension in FGI has sufficient distinguishable capability to detect malicious packages, and combining all dimensions in FGI cannot significantly improve overall performance.

Published
2024

5. OSS Malicious Package Analysis in the Wild

Author

Zhou, Xiaoyan, Zhang, Ying, Niu, Wenjia, Liu, Jiqiang, Wang, Haining, and Li, Qiang

Subjects
Computer Science - Cryptography and Security, Computer Science - Software Engineering
Abstract

The open-source software (OSS) ecosystem suffers from various security threats and risks, and malicious packages play a central role in software supply chain (SSC) attacks. Although malware research has a history of over thirty years, less attention has been paid to OSS malware. Its existing research has three limitations: a lack of high-quality datasets, malware diversity, and attack campaign context. In this paper, we first build and curate the largest dataset of 23,425 malicious packages from scattered online sources. We then propose a knowledge graph to represent the OSS malware corpus and conduct malicious package analysis in the wild. Our main findings include (1) it is essential to collect malicious packages from various online sources because there is little data overlap between different sources; (2) despite the sheer volume of SSC attack campaigns, many malicious packages are similar, and unknown/sophisticated attack behaviors have yet to emerge or be detected; (3) OSS malicious package has its distinct life cycle, denoted as {changing->release->detection->removal}, and slightly changing the package (different name) is a widespread attack manner; (4) while malicious packages often lack context about how and who released them, security reports disclose the information about corresponding SSC attack campaigns.

Published
2024

6. Nightfall Deception: A Novel Backdoor Attack on Traffic Sign Recognition Models via Low-Light Data Manipulation

Author

Wu, Yalun, Li, Qiong, Xiang, Yingxiao, Zheng, Jinkai, Wu, Xingyu, Han, Zhen, Liu, Jiqiang, Niu, Wenjia, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Quan Z., editor, Dobbie, Gill, editor, Jiang, Jing, editor, Zhang, Xuyun, editor, Zhang, Wei Emma, editor, Manolopoulos, Yannis, editor, Wu, Jia, editor, Mansoor, Wathiq, editor, and Ma, Congbo, editor

Published
2025
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7. ECG Signal Classification with a Multi-stage Model Integrating CNN, SNN, and ResNet

Author

Shi, Xiangyu, Cheng, Dianjing, Wu, Xingyu, Ye, Ping, Jia, Jingqi, Liu, Jiqiang, Niu, Wenjia, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Quan Z., editor, Dobbie, Gill, editor, Jiang, Jing, editor, Zhang, Xuyun, editor, Zhang, Wei Emma, editor, Manolopoulos, Yannis, editor, Wu, Jia, editor, Mansoor, Wathiq, editor, and Ma, Congbo, editor

Published
2025
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8. Knowledge-Driven Backdoor Removal in Deep Neural Networks via Reinforcement Learning

Author

Song, Jiayin, Li, Yike, Tian, Yunzhe, Wu, Xingyu, Li, Qiong, Tong, Endong, Niu, Wenjia, Zhang, Zhenguo, Liu, Jiqiang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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9. Lurking in the Shadows: Imperceptible Shadow Black-Box Attacks Against Lane Detection Models

Author

Cui, Xiaoshu, Wu, Yalun, Gu, Yanfeng, Li, Qiong, Tong, Endong, Liu, Jiqiang, Niu, Wenjia, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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10. Recognition of Signal Modulation Pattern Based on Multi-task Self-supervised Learning

Author

Cheng, Dianjing, Wu, Xingyu, Xie, Zhenghao, Cui, Zhihua, Li, Qiong, Tong, Endong, Niu, Wenjia, Wei, Ziyi, Zhao, Xinyi, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Torresen, Jim, editor, and Yang, Shengxiang, editor

Published
2024
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11. Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding

Author

Niu, Wenjia, Zhang, Kaihao, Luo, Wenhan, and Zhong, Yiran

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images. Single-Image SR generally handles each image independently, but ignores the temporal information implied in continuing frames. Multi-frame SR is able to model the temporal dependency via capturing motion information. However, it relies on neighbouring frames which are not always available in the real world. Meanwhile, slight camera shake easily causes heavy motion blur on long-distance-shot low-resolution images. To address these problems, a Blind Motion Deblurring Super-Reslution Networks, BMDSRNet, is proposed to learn dynamic spatio-temporal information from single static motion-blurred images. Motion-blurred images are the accumulation over time during the exposure of cameras, while the proposed BMDSRNet learns the reverse process and uses three-streams to learn Bidirectional spatio-temporal information based on well designed reconstruction loss functions to recover clean high-resolution images. Extensive experiments demonstrate that the proposed BMDSRNet outperforms recent state-of-the-art methods, and has the ability to simultaneously deal with image deblurring and SR., Comment: To appear in IEEE Transactions on Image Processing (TIP)

Published
2021
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12. GERNS: A Graph Embedding with Repeat-Free Neighborhood Structure for Subgraph Matching Optimization

Author

Chang, Yubiao, Wang, Tian, Tian, Chao, Zhan, Ding, Chen, Cui, Wu, Xingyu, Liu, Chunxia, Tong, Endong, Niu, Wenjia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Haofen, editor, Han, Xianpei, editor, Liu, Ming, editor, Cheng, Gong, editor, Liu, Yongbin, editor, and Zhang, Ningyu, editor

Published
2023
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13. LNFGP: Local Node Fusion-Based Graph Partition by Greedy Clustering

Author

Tian, Chao, Wang, Tian, Zhan, Ding, Chang, Yubiao, Wu, Xingyu, Chen, Cui, Cai, Xingjuan, Tong, Endong, Niu, Wenjia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Haofen, editor, Han, Xianpei, editor, Liu, Ming, editor, Cheng, Gong, editor, Liu, Yongbin, editor, and Zhang, Ningyu, editor

Published
2023
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14. Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

Author

Zhao, Di, Liu, Jiqiang, Wang, Jialin, Niu, Wenjia, Tong, Endong, Chen, Tong, and Li, Gang

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused huge losses inadvertently. However, to our knowledge, all previous works use common methods such as Causal-Correlation or Cased-based to detect outdated multi-stage attacks. Few attentions have been paid to detect the "Feint Attack", because the difficulty of detection lies in the diversification of the concept of "Feint Attack" and the lack of professional datasets, many detection methods ignore the semantic relationship in the attack. Aiming at the existing challenge, this paper explores a new method to solve the problem. In the attack scenario, the fuzzy clustering method based on attribute similarity is used to mine multi-stage attack chains. Then we use a few-shot deep learning algorithm (SMOTE&CNN-SVM) and bidirectional Recurrent Neural Network model (Bi-RNN) to obtain the "Feint Attack" chains. "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. So we used Bi-RNN coding to obtain the hidden feature of "Feint Attack" chain. In the end, our method achieved the goal to detect the "Feint Attack" accurately by using the LLDoS1.0 and LLDoS2.0 of DARPA2000 and CICIDS2017 of Canadian Institute for Cybersecurity.

Published
2019

15. Adapting on Road Traffic-Oriented Controlled Optimization of Phases to Heterogeneous Intersections

Author

Qiao, Ziyan, Sun, Rui, Chen, Shiyao, Zi, Dong, Wu, Xingyu, Wang, Qian, Tong, Endong, Niu, Wenjia, Liu, Jiqiang, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Zucker, Jean-Daniel, editor, and An, Bo, editor

Published
2022
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16. Predicting Congestion Attack of Variable Spoofing Frequency for Reliable Traffic Signal System

Author

Xiang, Yingxiao, Chen, Tong, Li, Yike, Tian, Yunzhe, Niu, Wenjia, Tong, Endong, Liu, Jiqiang, Jia, Bowei, Wu, Yalun, Huang, Xinyu, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Shi, Wenbo, editor, Chen, Xiaofeng, editor, and Choo, Kim-Kwang Raymond, editor

Published
2022
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17. Security Analysis of Poisoning Attacks Against Multi-agent Reinforcement Learning

Author

Xie, Zhiqiang, Xiang, Yingxiao, Li, Yike, Zhao, Shuang, Tong, Endong, Niu, Wenjia, Liu, Jiqiang, Wang, Jian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lai, Yongxuan, editor, Wang, Tian, editor, Jiang, Min, editor, Xu, Guangquan, editor, Liang, Wei, editor, and Castiglione, Aniello, editor

Published
2022
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18. A Training-based Identification Approach to VIN Adversarial Examples

Author

Wang, Yingdi, Niu, Wenjia, Chen, Tong, Xiang, Yingxiao, Liu, Jingjing, Li, Gang, and Liu, Jiqiang

Subjects
Computer Science - Cryptography and Security
Abstract

With the rapid development of Artificial Intelligence (AI), the problem of AI security has gradually emerged. Most existing machine learning algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. The adversarial examples pose a potential security threat for many AI application areas, especially in the domain of robot path planning. In this field, the adversarial examples obstruct the algorithm by adding obstacles to the normal maps, resulting in multiple effects on the predicted path. However, there is no suitable approach to automatically identify them. To our knowledge, all previous work uses manual observation method to estimate the attack results of adversarial maps, which is time-consuming. Aiming at the existing problem, this paper explores a method to automatically identify the adversarial examples in Value Iteration Networks (VIN), which has a strong generalization ability. We analyze the possible scenarios caused by the adversarial maps. We propose a training-based identification approach to VIN adversarial examples by combing the path feature comparison and path image classification. We evaluate our method using the adversarial maps dataset, show that our method can achieve a high-accuracy and faster identification than manual observation method.

Published
2018

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

Author

Chen, Tong, Niu, Wenjia, Xiang, Yingxiao, Bai, Xiaoxuan, Liu, Jiqiang, Han, Zhen, and Li, Gang

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning
Abstract

As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial examples are effective when targeting A3C path finding, and design a Common Dominant Adversarial Examples Generation Method (CDG) to generate dominant adversarial examples against any given map. 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. Extensive experimental results show that, the lowest generation precision for CDG algorithm is 91.91%, and the lowest immune precision for Gradient Band-based Adversarial Training is 93.89%, which can prove that our method can realize the generalized attack immunity of A3C path finding with a high confidence., Comment: 25 pages 14 figures

Published
2018

21. Collaborative Attack Sequence Generation Model Based on Multiagent Reinforcement Learning for Intelligent Traffic Signal System.

Author

Wu, Yalun, Xiang, Yingxiao, Baker, Thar, Tong, Endong, Zhu, Ye, Cui, Xiaoshu, Zhang, Zhenguo, Han, Zhen, Liu, Jiqiang, Niu, Wenjia, and Zhu, Konglin

Abstract

Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Reflective Adversarial Attacks against Pedestrian Detection Systems for Vehicles at Night.

Author

Chen, Yuanwan, Wu, Yalun, Cui, Xiaoshu, Li, Qiong, Liu, Jiqiang, and Niu, Wenjia

Subjects
PARTICLE swarm optimization, DIGITAL technology, REFLECTIVE materials, DEEP learning, OPTICAL materials, PEDESTRIANS
Abstract

The advancements in deep learning have significantly enhanced the accuracy and robustness of pedestrian detection. However, recent studies reveal that adversarial attacks can exploit the vulnerabilities of deep learning models to mislead detection systems. These attacks are effective not only in digital environments but also pose significant threats to the reliability of pedestrian detection systems in the physical world. Existing adversarial attacks targeting pedestrian detection primarily focus on daytime scenarios and are easily noticeable by road observers. In this paper, we propose a novel adversarial attack method against vehicle–pedestrian detection systems at night. Our approach utilizes reflective optical materials that can effectively reflect light back to its source. We optimize the placement of these reflective patches using the particle swarm optimization (PSO) algorithm and deploy patches that blend with the color of pedestrian clothing in real-world scenarios. These patches remain inconspicuous during the day or under low-light conditions, but at night, the reflected light from vehicle headlights effectively disrupts the vehicle's pedestrian detection systems. Considering that real-world detection models are often black-box systems, we propose a "symmetry" strategy, which involves using the behavior of an alternative model to simulate the response of the target model to adversarial patches. We generate adversarial examples using YOLOv5 and apply our attack to various types of pedestrian detection models. Experiments demonstrate that our approach is both effective and broadly applicable. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Improving Convolutional Neural Network-Based Webshell Detection Through Reinforcement Learning

Author

Wu, Yalun, Song, Minglu, Li, Yike, Tian, Yunzhe, Tong, Endong, Niu, Wenjia, Jia, Bowei, Huang, Haixiang, Li, Qiong, Liu, Jiqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gao, Debin, editor, Li, Qi, editor, Guan, Xiaohong, editor, and Liao, Xiaofeng, editor

Published
2021
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24. Bidirectional RNN-Based Few-Shot Training for Detecting Multi-stage Attack

Author

Jia, Bowei, Tian, Yunzhe, Zhao, Di, Wang, Xiaojin, Li, Chenyang, Niu, Wenjia, Tong, Endong, Liu, Jiqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Wu, Yongdong, editor

Published
2021
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27. Queue Length Estimation Based Defence Against Data Poisoning Attack for Traffic Signal Control

Author

Gao, Xu, Liu, Jiqiang, Li, Yike, Wang, Xiaojin, Xiang, YingXiao, Tong, Endong, Niu, Wenjia, Han, Zhen, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Vadera, Sunil, editor, and Chang, Elizabeth, editor

Published
2020
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28. A C-IFGSM Based Adversarial Approach for Deep Learning Based Intrusion Detection

Author

Wang, Yingdi, Wang, Yixiang, Tong, Endong, Niu, Wenjia, Liu, Jiqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ben Hedia, Belgacem, editor, Chen, Yu-Fang, editor, Liu, Gaiyun, editor, and Yu, Zhenhua, editor

Published
2020
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31. Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation

Author

Kang, Cuicui, Liao, Shengcai, He, Yonghao, Wang, Jian, Niu, Wenjia, Xiang, Shiming, and Pan, Chunhong

Subjects
Computer Science - Multimedia, Computer Science - Information Retrieval, Computer Science - Learning
Abstract

The cross-media retrieval problem has received much attention in recent years due to the rapid increasing of multimedia data on the Internet. A new approach to the problem has been raised which intends to match features of different modalities directly. In this research, there are two critical issues: how to get rid of the heterogeneity between different modalities and how to match the cross-modal features of different dimensions. Recently metric learning methods show a good capability in learning a distance metric to explore the relationship between data points. However, the traditional metric learning algorithms only focus on single-modal features, which suffer difficulties in addressing the cross-modal features of different dimensions. In this paper, we propose a cross-modal similarity learning algorithm for the cross-modal feature matching. The proposed method takes a bilinear formulation, and with the nuclear-norm penalization, it achieves low-rank representation. Accordingly, the accelerated proximal gradient algorithm is successfully imported to find the optimal solution with a fast convergence rate O(1/t^2). Experiments on three well known image-text cross-media retrieval databases show that the proposed method achieves the best performance compared to the state-of-the-art algorithms.

Published
2014

32. A DeepWalk-Based Approach to Defend Profile Injection Attack in Recommendation System

Author

Gao, Xu, Niu, Wenjia, Liu, Jingjing, Chen, Tong, Xiang, Yingxiao, Bai, Xiaoxuan, Liu, Jiqiang, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Shi, Zhongzhi, editor, Mercier-Laurent, Eunika, editor, and Li, Jiuyong, editor

Published
2018
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33. A Method to Effectively Detect Vulnerabilities on Path Planning of VIN

Author

Liu, Jingjing, Niu, Wenjia, Liu, Jiqiang, Zhao, Jia, Chen, Tong, Yang, Yinqi, Xiang, Yingxiao, Han, Lei, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Qing, Sihan, editor, Mitchell, Chris, editor, Chen, Liqun, editor, and Liu, Dongmei, editor

Published
2018
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34. A Cross-Modal CCA-Based Astroturfing Detection Approach

Author

Bai, Xiaoxuan, Xiang, Yingxiao, Niu, Wenjia, Liu, Jiqiang, Chen, Tong, Liu, Jingjing, Wu, Tong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Qing, Sihan, editor, Mitchell, Chris, editor, Chen, Liqun, editor, and Liu, Dongmei, editor

Published
2018
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37. Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers

Author

Tian, Yunzhe, Xu, Dongyue, Tong, Endong, Sun, Rui, Chen, Kang, Li, Yike, Baker, Thar, Niu, Wenjia, and Liu, Jiqiang

Abstract

Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions.

Published
2024
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42. Beyond the Aggregation of Its Members—A Novel Group Recommender System from the Perspective of Preference Distribution

Author

Guo, Zhiwei, Tang, Chaowei, Niu, Wenjia, Fu, Yunqing, Xia, Haiyang, Tang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Gang, editor, Ge, Yong, editor, Zhang, Zili, editor, Jin, Zhi, editor, and Blumenstein, Michael, editor

Published
2017
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43. A Hidden Astroturfing Detection Approach Base on Emotion Analysis

Author

Chen, Tong, Alallaq, Noora Hashim, Niu, Wenjia, Wang, Yingdi, Bai, Xiaoxuan, Liu, Jingjing, Xiang, Yingxiao, Wu, Tong, Liu, Jiqiang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Gang, editor, Ge, Yong, editor, Zhang, Zili, editor, Jin, Zhi, editor, and Blumenstein, Michael, editor

Published
2017
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44. You Can’t Hide: A Novel Methodology to Defend DDoS Attack Based on Botcloud

Author

Li, Baohui, Niu, Wenjia, Xu, Kefu, Zhang, Chuang, Zhang, Peng, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Niu, Wenjia, editor, Li, Gang, editor, Liu, Jiqiang, editor, Tan, Jianlong, editor, Guo, Li, editor, Han, Zhen, editor, and Batten, Lynn, editor

Published
2015
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45. Structural Analysis of IWA Social Network

Author

Liu, Wenpeng, Cao, Yanan, Li, Diying, Niu, Wenjia, Tan, Jianlong, Hu, Yue, Guo, Li, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Niu, Wenjia, editor, Li, Gang, editor, Liu, Jiqiang, editor, Tan, Jianlong, editor, Guo, Li, editor, Han, Zhen, editor, and Batten, Lynn, editor

Published
2015
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49. An Approach to Detect the Internet Water Army via Dirichlet Process Mixture Model Based GSP Algorithm

Author

Li, Dan, Li, Qian, Hu, Yue, Niu, Wenjia, Tan, Jianlong, Guo, Li, Junqueira Barbosa, Simone Diniz, Series editor, Chen, Phoebe, Series editor, Cuzzocrea, Alfredo, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Ślęzak, Dominik, Series editor, Washio, Takashi, Series editor, Yang, Xiaokang, Series editor, Batten, Lynn, editor, Li, Gang, editor, Niu, Wenjia, editor, and Warren, Matthew, editor

Published
2014
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50. Two-Phased Event Causality Acquisition: Coupling the Boundary Identification and Argument Identification Approaches

Author

Cao, Yanan, Cao, Cungen, Zhang, Jingzun, Niu, Wenjia, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Zhang, Songmao, editor, Wirsing, Martin, editor, and Zhang, Zili, editor

Published
2015
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