World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8858
World Ranking
7906
National Ranking
250

Jun Zhang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jun Zhang sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 153 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jun Zhang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jun Zhang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jun Zhang is affiliated with the Swinburne University of Technology in Australia and has contributed extensively to the field of computer science, with a primary focus on cybersecurity, artificial intelligence, and network security.

Their research spans multiple subfields of computer science, including:

  • Artificial Intelligence
  • Information Systems
  • Computer Networks and Communications
  • Signal Processing
  • Computer Vision and Pattern Recognition

Jun Zhang's work concentrates on topics such as:

  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection
  • Software Engineering Research
  • Internet Traffic Analysis and Secure E-voting
  • Spam and Phishing Detection
  • Privacy-Preserving Technologies in Data
  • Security and Verification in Computing

Frequent co-authors in Jun Zhang's publications include:

  • Yang Xiang (23 collaborations)
  • Chao Chen (12 collaborations)
  • Lei Pan (11 collaborations)
  • Leo Yu Zhang (10 collaborations)
  • Shigang Liu (7 collaborations)

Jun Zhang has published numerous papers in well-known academic venues. The most common publication venues include:

  • arXiv (Cornell University) - 9 publications
  • ACM Computing Surveys - 4 publications
  • IEEE Transactions on Dependable and Secure Computing - 4 publications
  • Computers & Security - 3 publications
  • Neural Computing and Applications - 2 publications

Notable recent papers authored by or including Jun Zhang are:

  • "Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey", 2021, IEEE/CAA Journal of Automatica Sinica
  • "Software Vulnerability Detection Using Deep Neural Networks: A Survey", 2020, Proceedings of the IEEE
  • "Security and privacy in 6G networks: New areas and new challenges", 2020, Digital Communications and Networks
  • "A Survey of Android Malware Detection with Deep Neural Models", 2020, ACM Computing Surveys
  • "Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives", 2023, IEEE Communications Surveys & Tutorials

Jun Zhang's expertise lies primarily within computer science, with 145 publications contributing to this broad field. Their work integrates areas of advanced malware detection, network intrusion detection, privacy-preserving methods, and secure communication technologies.

Best Publications

  • Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

    Hong Lai;Jun Zhang;Ming Xing Luo;Lei Pan

  • Network Traffic Classification Using Correlation Information

    Jun Zhang;Yang Xiang;Yu Wang;Wanlei Zhou

  • Robust network traffic classification

    Jun Zhang;Xiao Chen;Yang Xiang;Wanlei Zhou

  • Software Vulnerability Detection Using Deep Neural Networks: A Survey

    Guanjun Lin;Sheng Wen;Qing-Long Han;Jun Zhang

  • Detecting and Preventing Cyber Insider Threats: A Survey

    Liu Liu;Olivier De Vel;Qing-Long Han;Jun Zhang

  • Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey

    Jun Zhang;Lei Pan;Qing-Long Han;Chao Chen

  • Security and privacy in 6G networks: New areas and new challenges

    Minghao Wang;Tianqing Zhu;Tao Zhang;Jun Zhang

  • Data-Driven Cybersecurity Incident Prediction: A Survey

    Nan Sun;Jun Zhang;Paul Rimba;Shang Gao

  • Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

    Xiao Chen;Chaoran Li;Derui Wang;Sheng Wen

  • A Survey of Android Malware Detection with Deep Neural Models

    Junyang Qiu;Jun Zhang;Wei Luo;Lei Pan

  • Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

    Jun Zhang;Chao Chen;Yang Xiang;Wanlei Zhou

  • Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

    Guanjun Lin;Jun Zhang;Wei Luo;Lei Pan

  • Twitter spam detection based on deep learning

    Tingmin Wu;Shigang Liu;Jun Zhang;Yang Xiang

  • Statistical Features-Based Real-Time Detection of Drifted Twitter Spam

    Chao Chen;Yu Wang;Jun Zhang;Yang Xiang

  • 6 million spam tweets: A large ground truth for timely Twitter spam detection

    Chao Chen;Jun Zhang;Xiao Chen;Yang Xiang

  • An Effective Network Traffic Classification Method with Unknown Flow Detection

    Jun Zhang;Chao Chen;Yang Xiang;Wanlei Zhou

  • A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection

    Chao Chen;Jun Zhang;Yi Xie;Yang Xiang

  • Addressing the class imbalance problem in Twitter spam detection using ensemble learning

    Shigang Liu;Yu Wang;Jun Zhang;Chao Chen

  • Modeling Propagation Dynamics of Social Network Worms

    Sheng Wen;Wei Zhou;Jun Zhang;Yang Xiang

  • Internet Traffic Classification Using Constrained Clustering

    Yu Wang;Yang Xiang;Jun Zhang;Wanlei Zhou

Frequent Co-Authors

Yang Xiang
Yang Xiang Swinburne University of Technology
Wanlei Zhou
Wanlei Zhou City University of Macau
Josef Pieprzyk
Josef Pieprzyk Commonwealth Scientific and Industrial Research Organisation
Mehmet A. Orgun
Mehmet A. Orgun Macquarie University
Qing-Long Han
Qing-Long Han Swinburne University of Technology
Surya Nepal
Surya Nepal Commonwealth Scientific and Industrial Research Organisation
Mohammad Mehedi Hassan
Mohammad Mehedi Hassan King Saud University
Yong Xiang
Yong Xiang Deakin University
Chin-Chen Chang
Chin-Chen Chang Feng Chia University
Abdulhameed Alelaiwi
Abdulhameed Alelaiwi King Saud University

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