Gang Shi

497 total citations
44 papers, 298 citations indexed

About

Gang Shi is a scholar working on Signal Processing, Information Systems and Artificial Intelligence. According to data from OpenAlex, Gang Shi has authored 44 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Signal Processing, 23 papers in Information Systems and 22 papers in Artificial Intelligence. Recurrent topics in Gang Shi's work include Advanced Malware Detection Techniques (26 papers), Security and Verification in Computing (17 papers) and Software Reliability and Analysis Research (9 papers). Gang Shi is often cited by papers focused on Advanced Malware Detection Techniques (26 papers), Security and Verification in Computing (17 papers) and Software Reliability and Analysis Research (9 papers). Gang Shi collaborates with scholars based in China, Japan and United Kingdom. Gang Shi's co-authors include Baolei Xu, Zhidong Wang, Yunfa Fu, Changhao Jiang, Hongyi Li, Liwei Chen, Dan Meng, Dan Meng, Liwei Chen and Rui Hou and has published in prestigious journals such as IEEE Access, IEEE Transactions on Computers and Applied Soft Computing.

In The Last Decade

Gang Shi

37 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gang Shi China 10 117 93 89 80 71 44 298
Mohammed Abubaker Türkiye 7 87 0.7× 54 0.6× 25 0.3× 20 0.3× 16 0.2× 15 288
Massimo Narizzano Italy 11 115 1.0× 104 1.1× 14 0.2× 16 0.2× 36 0.5× 23 377
Adam Page United States 10 190 1.6× 50 0.5× 71 0.8× 72 0.9× 6 0.1× 14 351
Hrishikesh Deshpande United States 8 61 0.5× 23 0.2× 62 0.7× 26 0.3× 22 0.3× 15 403
Qingling Cai China 10 66 0.6× 104 1.1× 34 0.4× 8 0.1× 49 0.7× 23 291
Samed Jukić Bosnia and Herzegovina 6 60 0.5× 161 1.7× 113 1.3× 12 0.1× 18 0.3× 13 287
Luke Everson United States 9 73 0.6× 34 0.4× 18 0.2× 251 3.1× 10 0.1× 21 451
Mahbubul Alam United States 9 70 0.6× 89 1.0× 58 0.7× 23 0.3× 63 0.9× 28 311
Sheikh Md. Rabiul Islam Bangladesh 8 84 0.7× 25 0.3× 44 0.5× 56 0.7× 29 0.4× 53 208
Bharti Rana India 11 80 0.7× 27 0.3× 21 0.2× 12 0.1× 56 0.8× 27 320

Countries citing papers authored by Gang Shi

Since Specialization
Citations

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

Fields of papers citing papers by Gang Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gang Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Gang Shi. A scholar is included among the top collaborators of Gang Shi 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 Gang Shi. Gang Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Liwei, et al.. (2024). SCFI: Efficient forward fine-grained control flow integrity based on coarse-grained ISA extensions. Computers & Security. 140. 103800–103800.
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Cui, Ningning, Liwei Chen, & Gang Shi. (2023). BVSNO: Binary Code Vulnerability Detection Based on Slice Semantic and Node Order. 373–379. 1 indexed citations
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Chen, Liwei, et al.. (2023). KPDFI: Efficient data flow integrity based on key property against data corruption attack. Computers & Security. 128. 103183–103183. 1 indexed citations
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Chen, Liwei, et al.. (2022). An Effective Buffer Overflow Detection With Super Data-Flow Graphs. 29. 684–691. 1 indexed citations
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Cui, Ningning, et al.. (2022). BHMVD: Binary Code-based Hybrid Neural Network for Multiclass Vulnerability Detection. abs/1807. 06756. 238–245. 1 indexed citations
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Chen, Liwei, et al.. (2021). Twine Stack: A Hybrid Mechanism Achieving Less Cost for Return Address Protection. 7–12. 1 indexed citations
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Chen, Liwei, et al.. (2021). Self-Attention based Automated Vulnerability Detection with Effective Data Representation. 892–899. 6 indexed citations
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Meng, Dan, Rui Hou, Gang Shi, et al.. (2020). Built-in Security Computer: Deploying Security-First Architecture Using Active Security Processor. IEEE Transactions on Computers. 69(11). 1571–1583. 10 indexed citations
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Chen, Liwei, et al.. (2020). ABCFI: Fast and Lightweight Fine-Grained Hardware-Assisted Control-Flow Integrity. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39(11). 3165–3176. 11 indexed citations
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Lin, Zheng, et al.. (2019). A Robust Adversarial Reinforcement Framework for Reading Comprehension. abs 1810 4805. 752–759. 1 indexed citations
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Guo, Yingjie, Liwei Chen, & Gang Shi. (2018). Function-Oriented Programming: A New Class of Code Reuse Attack in C Applications. 1–9. 9 indexed citations
17.
Zhu, Jianping, Wei Song, Ziyuan Zhu, et al.. (2018). CPU Security Benchmark. 2016. 8–14. 5 indexed citations
18.
Xu, Baolei, Changhao Jiang, Yunfa Fu, et al.. (2015). A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching. Journal of Neural Engineering. 12(3). 36004–36004. 104 indexed citations
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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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