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3. Malicious Attack Detection Method for Recommendation Systems Based on Meta-pseudo Labels and Dynamic Features

Author

Liu, Hui, Ji, Ke, Chen, Zhenxiang, Ma, Kun, Zhao, Xiaofan, 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, Zhang, Wenjie, editor, Tung, Anthony, editor, Zheng, Zhonglong, editor, Yang, Zhengyi, editor, Wang, Xiaoyang, editor, and Guo, Hongjie, editor

Published
2024
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4. KGhish: A Phishing Website Detection Method Based on Knowledge Graph

Author

Liu, Changlin, Wang, Shanshan, Chen, Zhenxiang, Huang, Limei, Li, Yan, Li, Hanwen, 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, Huang, De-Shuang, editor, Chen, Wei, editor, and Zhang, Qinhu, editor

Published
2024
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5. Constructing Adversarial Network Attacks in Realistic Network Environments

Author

Li, Hanwen, Wang, Shanshan, Han, Yadi, Liu, Changlin, Chen, Zhenxiang, 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, Huang, De-Shuang, editor, Chen, Wei, editor, and Guo, Jiayang, editor

Published
2024
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6. MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection

Author

Yu, Yongxin, Ji, Ke, Gao, Yuan, Chen, Zhenxiang, Ma, Kun, Wu, Jun, 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, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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7. Practical Single-Round Secure Wildcard Pattern Matching

Author

Xu, Jun, Zhao, Shengnan, Zhao, Chuan, Chen, Zhenxiang, Liu, Zhe, Fang, Liming, 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, Meyer, Norbert, editor, and Grocholewska-Czuryło, Anna, editor

Published
2024
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8. Low-Frequency Aware Unsupervised Detection of Dark Jargon Phrases on Social Platforms

Author

Huang, Limei, Wang, Shanshan, Liu, Changlin, Cao, Xueyang, Han, Yadi, Liu, Shaolei, Chen, Zhenxiang, 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, Liu, Fenrong, editor, Sadanandan, Arun Anand, editor, Pham, Duc Nghia, editor, Mursanto, Petrus, editor, and Lukose, Dickson, editor

Published
2024
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9. Devils in the Clouds: An Evolutionary Study of Telnet Bot Loaders

Author

Zhu, Yuhui, Chen, Zhenxiang, Yan, Qiben, Wang, Shanshan, Giaretta, Alberto, Li, Enlong, Peng, Lizhi, Zhao, Chuan, and Conti, Mauro

Subjects
Computer Science - Cryptography and Security
Abstract

One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Different approaches are necessary for studying the loaders evolution and defining a genealogy. To address the insufficient knowledge about loaders' lineage in existing studies, in this paper, we propose a semantic-aware method to measure, categorize, and compare different loader servers, with the goal of highlighting their evolution, independent from the payload evolution. Leveraging behavior-based metrics, we cluster the discovered loaders and define eight families to determine the genealogy and draw a homology map. Our study shows that the source code of Mirai is evolving and spawning new botnets with new capabilities, both on the client side and the server side. In turn, shedding light on the infection loaders can help the cybersecurity community to improve detection and prevention tools., Comment: 10 pages, 5 figures, ICC 2023. arXiv admin note: text overlap with arXiv:2206.00385

Published
2022

10. Mining Function Homology of Bot Loaders from Honeypot Logs

Author

Zhu, Yuhui, Chen, Zhenxiang, Yan, Qiben, Wang, Shanshan, Li, Enlong, Peng, Lizhi, and Zhao, Chuan

Subjects
Computer Science - Cryptography and Security
Abstract

Self-contained loaders are widely adopted in botnets for injecting loading commands and spawning new bots. While researchers can dissect bot clients to get various information of botnets, the cloud-based and self-contained design of loaders effectively hinders researchers from understanding the loaders' evolution and variation using classic methods. The decoupled nature of bot loaders also dramatically reduces the feasibility of investigating relationships among clients and infrastructures. In this paper, we propose a text-based method to investigate and analyze details of bot loaders using honeypots. We leverage high interaction honeypots to collect request logs and define eight families of bot loaders based on the result of agglomerative clustering. At the function level, we push our study further to explore their homological relationship based on similarity analysis of request logs using sequence aligning techniques. This further exploration discloses that the released code of Mirai keeps spawning new generations of botnets both on the client and the server side. This paper uncovers the homology of active botnet infrastructures, providing a new prospect on finding covert relationships among cybercrimes. Bot loaders are precisely investigated at the function level to yield a new insight for researchers to identify the botnet's infrastructures and track their evolution over time.

Published
2022

19. Fake News Detection Based on the Correlation Extension of Multimodal Information

Author

Li, Yanqiang, Ji, Ke, Ma, Kun, Chen, Zhenxiang, Zhou, Jin, Wu, Jun, 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, Li, Bohan, editor, Yue, Lin, editor, Tao, Chuanqi, editor, Han, Xuming, editor, Calvanese, Diego, editor, and Amagasa, Toshiyuki, editor

Published
2023
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24. Efficient Federated Learning Framework Based on Multi-Key Homomorphic Encryption

Author

Zhang, Qian, Jing, Shan, Zhao, Chuan, Zhang, Bo, Chen, Zhenxiang, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Barolli, Leonard, editor

Published
2022
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25. Expert Recommendation Algorithm Combining Attention and Recurrent Neural Network

Author

LYU Xiaoqi, JI Ke, CHEN Zhenxiang, SUN Runyuan, MA Kun, WU Jun, LI Yidong

Subjects
community question answering (cqa), expert recommendation, deep learning, attention mechanism, recurrent neural network (rnn), Electronic computers. Computer science, QA75.5-76.95
Abstract

Community question answering (CQA) has become the most important knowledge sharing and exchange platform on the Internet, effectively recommending massive questions raised by users to users who may answer them, and mining the questions that users are interested in is the core function of such platforms. Some expert recommendation algorithms for CQA have been proposed to improve the efficiency of platform answering, but most of the existing work focuses on matching user interests with question information, ignoring the dynamic change of user interests, which may seriously affect the quality of recommendation. This paper proposes an expert recommen-dation algorithm combining attention and recurrent neural network (RNN), which not only realizes deep feature coding of the question content, but also captures the dynamically changing user interest. First, the question encoder combines convolutional neural network (CNN) and Attention mechanism on the basis of pre-trained word embeddings to realize joint representation of deep feature of question title and bound topics. Then, the user encoder captures the dynamic interest using Bi-GRU model on the time series of user's historical answers to questions, and combines the user's fixed label information to represent the long-term interest. Finally, a recommendation result combining the user's dynamic interest and long-term interest is generated according to the similarity calculation of output vectors of two encoders. This paper conducts comparative experiments on different parameter configurations and different algorithms on real data from Zhihu. Experimental results show that the performance of the algorithm is significantly better than the current popular deep learning expert recommendation methods.

Published
2022
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27. Expert Recommendations with Temporal Dynamics of User Interest in CQA

Author

Lv, Xiaoqi, Ji, Ke, Chen, Zhenxiang, Ma, Kun, Wu, Jun, Li, Yidong, Xu, Guandong, 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, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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28. HACK: A Hierarchical Model for Fake News Detection

Author

Li, Yanqi, Ji, Ke, Ma, Kun, Chen, Zhenxiang, Wu, Jun, Li, Yidong, Xu, Guandong, 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, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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29. TranFuzz: An Ensemble Black-Box Attack Framework Based on Domain Adaptation and Fuzzing

Author

Li, Hao, Guo, Shanqing, Tang, Peng, Hu, Chengyu, Chen, Zhenxiang, 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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30. An Arcloss-Based and Openset-Test-Oriented Finger Vein Recognition System

Author

Chen, Zhenxiang, Yu, Wangwang, Bai, Haohan, Li, Yongjie, 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, Feng, Jianjiang, editor, Zhang, Junping, editor, Liu, Manhua, editor, and Fang, Yuchun, editor

Published
2021
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33. Cluster Center Initialization and Outlier Detection Based on Distance and Density for the K-Means Algorithm

Author

He, Qi, Chen, Zhenxiang, Ji, Ke, Wang, Lin, Ma, Kun, Zhao, Chuan, Shi, Yuliang, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Cherukuri, Aswani Kumar, editor, and Gandhi, Niketa, editor

Published
2020
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34. Improving Nearest Neighbor Partitioning Neural Network Classifier Using Multi-layer Particle Swarm Optimization

Author

Zhu, Xuehui, Zhang, He, Wang, Lin, Yang, Bo, Zhou, Jin, Chen, Zhenxiang, Abraham, Ajith, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Madureira, Ana Maria, editor, Abraham, Ajith, editor, Gandhi, Niketa, editor, and Varela, Maria Leonilde, editor

Published
2020
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35. Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage

Author

Ge, Yong-Feng, Cao, Jinli, Wang, Hua, Zhang, Yanchun, Chen, Zhenxiang, 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, Huang, Zhisheng, editor, Beek, Wouter, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
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39. Classify Sina Weibo users into High or Low happiness Groups Using Linguistic and Behavior Features

Author

Wang, Jingying, Liu, Tianli, Zhu, Tingshao, Zhang, Lei, Hao, Bibo, and Chen, Zhenxiang

Subjects
Computer Science - Social and Information Networks
Abstract

It's of great importance to measure happiness of social network users, but the existing method based on questionnaires suffers from high costs and low efficiency. This paper aims at identifying social network users' happiness level based on their Web behavior. We recruited 548 participants to fill in the Oxford Happiness Inventory (OHI) and divided them into two groups with high/low OHI score. We downloaded each Weibo user's data by calling API, and extracted 103 linguistic and behavior features. 24 features are identified with significant difference between high and low happiness groups. We trained a Decision Tree on these 24 features to make the prediction of high/low happiness group. The decision tree can be used to identify happiness level of any new social network user based on linguistic and behavior features. The Decision Tree can achieve 67.7% on precision. Although the capability of our Decision Tree is not ideal, classifying happiness via linguistic and behavior features on the Internet is proved to be feasible., Comment: 12 pages, 5 tables, 1 figures, typo fixed, the indexs in table 2 revised, four authors added

Published
2015

42. Quantifying and analysing the angular momentum in volleyball jump serve during the aerial phase: relationship to arm swing speed.

Author

Liu, Lingjun, Chen, Zhenxiang, Zhao, Defeng, Tan, Zhizong, and Qi, Yaqian

Subjects
ANGULAR momentum (Mechanics), VOLLEYBALL techniques, MOMENTUM transfer, VOLLEYBALL players, REGRESSION analysis, WRIST
Abstract

Background: In volleyball, the jump serve is a crucial and commonly used serving technique. Nonetheless, the angular momentum developed during the jump serve remains unexplored. The objectives of the current study were to determine the angular momentum manifesting during the airborne phase of the jump serve and to analyse the correlations between the angular momentum variables and arm swing speed. Methods: Three-dimensional coordinate data were obtained during the jump serves of 17 professional male volleyball players. Correlation and linear regression analyses were used to identify the angular momentum variables linked to the arm swing speed at ball impact (BI). Results: The arm swing speed at BI exhibited significant correlations with the peak angular momentum of the attack arm (r = 0.551, p = 0.024), non-attack arm (r = 0.608, p = 0.011), non-attack leg (r = −0.516, p = 0.034), forearm (r = 0.527, p = 0.032), and hand (r = 0.824, p < 0.001). A stepwise regression model (R2 = 0.35, p = 0.043) predicted arm swing speed based on the peak angular momentum of the non-attack leg, forearm, and hand. Conclusions: The study results suggest that during the arm-acceleration phase, (1) increasing angular momentum with the non-attack leg helps maintain aerial body balance, thereby enhancing arm swing execution, and (2) controlling the magnitude and timing of the force exerted by the elbow and wrist is crucial for effectively transmitting angular momentum, contributing to an increase in arm swing speed. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Towards Secure Computation of Similar Patient Query on Genomic Data Under Multiple Keys

Author

Zhao, Chuan, Zhao, Shengnan, Zhang, Bo, Jing, Shan, Chen, Zhenxiang, Zhao, Minghao, 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, Vaidya, Jaideep, editor, Zhang, Xiao, editor, and Li, Jin, editor

Published
2019
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