Pengbin Feng

453 total citations
14 papers, 285 citations indexed

About

Pengbin Feng is a scholar working on Signal Processing, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Pengbin Feng has authored 14 papers receiving a total of 285 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Signal Processing, 9 papers in Computer Networks and Communications and 6 papers in Information Systems. Recurrent topics in Pengbin Feng's work include Advanced Malware Detection Techniques (10 papers), Network Security and Intrusion Detection (8 papers) and Digital and Cyber Forensics (4 papers). Pengbin Feng is often cited by papers focused on Advanced Malware Detection Techniques (10 papers), Network Security and Intrusion Detection (8 papers) and Digital and Cyber Forensics (4 papers). Pengbin Feng collaborates with scholars based in China, United States and Australia. Pengbin Feng's co-authors include Jianfeng Ma, Cong Sun, Xinpeng Xu, Teng Li, Ning Xi, Kun Sun, Jiawei Zhang, Di Lu, Jianfeng Ma and Di Lu and has published in prestigious journals such as IEEE Access, The American Journal of the Medical Sciences and Computers & Security.

In The Last Decade

Pengbin Feng

13 papers receiving 274 citations

Peers

Pengbin Feng

SP

CNC

IS

AI

SOFTW

Victor Heorhiadi United States

Donghai Tian China

R.C. Durst United States

Shuaifu Dai China

Richard Skowyra United States

Jagsir Singh India

Tongbo Luo United States

Heesook Choi United States

Justin Sahs United States

Victor Heorhiadi United States

Pengbin Feng

43 ×0.2

SP

219 ×1.0

CNC

114 ×0.9

IS

71 ×1.0

AI

25 ×0.4

SOFTW

Citations per year, relative to Pengbin Feng Pengbin Feng (= 1×) peers Victor Heorhiadi

Countries citing papers authored by Pengbin Feng

Since Specialization

Citations

This map shows the geographic impact of Pengbin Feng'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 Pengbin Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pengbin Feng more than expected).

Fields of papers citing papers by Pengbin Feng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pengbin Feng. 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 Pengbin Feng. The network helps show where Pengbin Feng may publish in the future.

Co-authorship network of co-authors of Pengbin Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Pengbin Feng. A scholar is included among the top collaborators of Pengbin Feng 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 Pengbin Feng. Pengbin Feng 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:

14 of 14 papers shown

1.

Feng, Pengbin, Li Yang, Qin Wang, et al.. (2024). DawnGNN: Documentation augmented windows malware detection using graph neural network. Computers & Security. 140. 103788–103788. 12 indexed citations

2.

Feng, Pengbin, et al.. (2024). Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt. 726–730. 4 indexed citations

3.

Feng, Pengbin, Li Yang, Di Lu, Ning Xi, & Jianfeng Ma. (2023). BejaGNN: behavior-based Java malware detection via graph neural network. The Journal of Supercomputing. 79(14). 15390–15414. 7 indexed citations

4.

Li, Yuxi, Pengbin Feng, Qingjie Chen, et al.. (2022). Comparative effectiveness and safety of bolus vs. continuous infusion of loop diuretics: Results from the MIMIC-III Database. The American Journal of the Medical Sciences. 365(4). 353–360. 2 indexed citations

5.

Feng, Pengbin, et al.. (2022). Enhancing malware analysis sandboxes with emulated user behavior. Computers & Security. 115. 102613–102613. 25 indexed citations

6.

Wang, Shu, et al.. (2021). PatchRNN: A Deep Learning-Based System for Security Patch Identification. arXiv (Cornell University). 595–600. 1 indexed citations

7.

Feng, Pengbin, et al.. (2021). HoneyBog: A Hybrid Webshell Honeypot Framework against Command Injection. 218–226. 3 indexed citations

8.

Feng, Pengbin, Jianfeng Ma, Teng Li, et al.. (2021). Android Malware Detection via Graph Representation Learning. Mobile Information Systems. 2021. 1–14. 12 indexed citations

9.

Feng, Pengbin, Jianfeng Ma, Teng Li, et al.. (2020). Android Malware Detection Based on Call Graph via Graph Neural Network. 368–374. 11 indexed citations

10.

Li, Teng, et al.. (2019). Lightweight Security Authentication Mechanism Towards UAV Networks. 379–384. 47 indexed citations

11.

Feng, Pengbin, et al.. (2018). A Novel Dynamic Android Malware Detection System With Ensemble Learning. IEEE Access. 6. 30996–31011. 152 indexed citations

12.

Sun, Cong, Pengbin Feng, Teng Li, & Jianfeng Ma. (2017). Data-Oriented Instrumentation against Information Leakages of Android Applications. 485–490. 1 indexed citations

13.

Feng, Pengbin, Jianfeng Ma, & Cong Sun. (2017). Selecting Critical Data Flows in Android Applications for Abnormal Behavior Detection. Mobile Information Systems. 2017. 1–16. 7 indexed citations

14.

Feng, Pengbin, Cong Sun, & Jianfeng Ma. (2016). Measuring the risk value of sensitive dataflow path in Android applications. Security and Communication Networks. 9(18). 5918–5933. 1 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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