Xinjun Pei

578 total citations
23 papers, 358 citations indexed

About

Xinjun Pei is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Xinjun Pei has authored 23 papers receiving a total of 358 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Networks and Communications, 12 papers in Information Systems and 12 papers in Artificial Intelligence. Recurrent topics in Xinjun Pei's work include Network Security and Intrusion Detection (14 papers), Advanced Malware Detection Techniques (11 papers) and Privacy-Preserving Technologies in Data (6 papers). Xinjun Pei is often cited by papers focused on Network Security and Intrusion Detection (14 papers), Advanced Malware Detection Techniques (11 papers) and Privacy-Preserving Technologies in Data (6 papers). Xinjun Pei collaborates with scholars based in China, United States and Denmark. Xinjun Pei's co-authors include Shengwei Tian, Xiaoheng Deng, Long Yu, Kaiping Xue, Shengwei Tian, Lan Zhang, Huanhuan Wang, Long Yu, Zhen Ling and Ping Jiang and has published in prestigious journals such as IEEE Access, IEEE Transactions on Industrial Informatics and IEEE Transactions on Information Forensics and Security.

In The Last Decade

Xinjun Pei

20 papers receiving 349 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Xinjun Pei China	10	236	199	186	150	23	23	358
Nurbol Luktarhan China	11	291 1.2×	203 1.0×	271 1.5×	118 0.8×	18 0.8×	24	422
Hamad Binsalleeh Canada	11	303 1.3×	236 1.2×	352 1.9×	221 1.5×	29 1.3×	18	529
Hao Fu United States	9	147 0.6×	101 0.5×	83 0.4×	104 0.7×	34 1.5×	16	231
Faheem Ullah Australia	8	228 1.0×	129 0.6×	119 0.6×	131 0.9×	34 1.5×	23	311
Anyi Liu United States	10	210 0.9×	160 0.8×	132 0.7×	220 1.5×	8 0.3×	24	370
Futai Zou China	14	326 1.4×	205 1.0×	293 1.6×	219 1.5×	21 0.9×	66	568
Ahsan Nazir China	11	162 0.7×	114 0.6×	119 0.6×	96 0.6×	26 1.1×	23	279
Deepak Kshirsagar India	10	349 1.5×	258 1.3×	268 1.4×	78 0.5×	28 1.2×	29	411
Qiujian Lv China	8	241 1.0×	176 0.9×	129 0.7×	100 0.7×	6 0.3×	32	370
Morteza Safaei Pour United States	10	205 0.9×	155 0.8×	138 0.7×	72 0.5×	37 1.6×	19	282

Countries citing papers authored by Xinjun Pei

Since Specialization

Citations

This map shows the geographic impact of Xinjun Pei's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xinjun Pei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xinjun Pei more than expected).

Fields of papers citing papers by Xinjun Pei

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xinjun Pei. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xinjun Pei. The network helps show where Xinjun Pei may publish in the future.

Co-authorship network of co-authors of Xinjun Pei

This figure shows the co-authorship network connecting the top 25 collaborators of Xinjun Pei. A scholar is included among the top collaborators of Xinjun Pei based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xinjun Pei. Xinjun Pei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Tian, Shengwei, et al.. (2025). Client-Level Fault-Tolerant Federated Semi-Supervised Learning for Unlabeled Clients in Internet of Vehicles. IEEE Transactions on Network Science and Engineering. 12(6). 4657–4670.

Deng, Xiaoheng, et al.. (2025). Securing internet of vehicles: a blockchain-based federated learning approach for enhanced intrusion detection. Cluster Computing. 28(4). 4 indexed citations

Deng, Xiaoheng, et al.. (2025). A blockchain-based federated learning framework against poisoning attacks in the internet of vehicles. Computer Networks. 272. 111705–111705.

Deng, Xiaoheng, et al.. (2024). E-DBRL: efficient double broad reinforcement learning for adaptive traffic signal control. Applied Intelligence. 54(17-18). 8563–8575. 1 indexed citations

Pei, Xinjun, et al.. (2024). A Privacy-Preserving Graph Neural Network for Network Intrusion Detection. IEEE Transactions on Dependable and Secure Computing. 22(1). 740–756. 4 indexed citations

Pei, Xinjun, Xiaoheng Deng, Naixue Xiong, Shahid Mumtaz, & Jie Wu. (2024). Complex Graph Analysis and Representation Learning: Problems, Techniques, and Applications. IEEE Transactions on Network Science and Engineering. 11(5). 4990–5007. 5 indexed citations

Liu, Yi‐Jing, et al.. (2024). Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing. 24316–24325. 1 indexed citations

Chen, Peng, et al.. (2024). Efficient malware detection through inter-component communication analysis. Cluster Computing. 27(8). 11667–11682.

Pei, Xinjun, Xiaoheng Deng, Shengwei Tian, Jianqing Liu, & Kaiping Xue. (2023). Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs. IEEE Transactions on Information Forensics and Security. 19. 1614–1629. 14 indexed citations

10.

Deng, Xiaoheng, et al.. (2023). TransMalDE: An Effective Transformer Based Hierarchical Framework for IoT Malware Detection. IEEE Transactions on Network Science and Engineering. 11(1). 140–151. 9 indexed citations

11.

Pei, Xinjun, Xiaoheng Deng, Shengwei Tian, & Kaiping Xue. (2023). Efficient Privacy Preserving Graph Neural Network for Node Classification. 1–5. 2 indexed citations

12.

Deng, Xiaoheng, et al.. (2023). A verifiable and privacy-preserving blockchain-based federated learning approach. Peer-to-Peer Networking and Applications. 16(5). 2256–2270. 21 indexed citations

13.

Tian, Shengwei, et al.. (2023). AdaTrans: An adaptive transformer for IoT Malware detection based on sensitive API call graph and inter-component communication analysis. Journal of Intelligent & Fuzzy Systems. 45(6). 11439–11452. 1 indexed citations

14.

Deng, Xiaoheng, et al.. (2022). Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT Networks. IEEE Transactions on Network and Service Management. 20(1). 684–696. 41 indexed citations

15.

Deng, Xiaoheng, Xinjun Pei, Shengwei Tian, & Lan Zhang. (2022). Edge-Based IIoT Malware Detection for Mobile Devices With Offloading. IEEE Transactions on Industrial Informatics. 19(7). 8093–8103. 11 indexed citations

16.

Pei, Xinjun, Xiaoheng Deng, Shengwei Tian, Lan Zhang, & Kaiping Xue. (2022). A Knowledge Transfer-based Semi-Supervised Federated Learning for IoT Malware Detection. IEEE Transactions on Dependable and Secure Computing. 1–1. 52 indexed citations

17.

Tian, Shengwei, et al.. (2021). Malicious URL Detection Based on a Parallel Neural Joint Model. IEEE Access. 9. 9464–9472. 43 indexed citations

18.

Pei, Xinjun, et al.. (2020). A Two-Stream Network Based on Capsule Networks and Sliced Recurrent Neural Networks for DGA Botnet Detection. Journal of Network and Systems Management. 28(4). 1694–1721. 14 indexed citations

19.

Pei, Xinjun, Long Yu, & Shengwei Tian. (2020). AMalNet: A deep learning framework based on graph convolutional networks for malware detection. Computers & Security. 93. 101792–101792. 78 indexed citations

20.

Wang, Huanhuan, et al.. (2019). Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network. Applied Intelligence. 49(8). 3016–3026. 34 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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