World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
14104
World Ranking
6332
National Ranking
100

Asaf Shabtai 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 Asaf Shabtai 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: 308 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Asaf Shabtai 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 Asaf Shabtai 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Asaf Shabtai is affiliated with Ben-Gurion University of the Negev in Israel, focusing primarily on computer science research. Their work encompasses a range of subfields, including artificial intelligence, computer networks and communications, information systems, signal processing, and computer vision and pattern recognition.

Their research extensively covers various topics such as network security and intrusion detection, adversarial robustness in machine learning, advanced malware detection techniques, anomaly detection techniques and applications, information and cyber security, internet traffic analysis and secure e-voting, and smart grid security and resilience.

Shabtai has contributed to numerous publications, with recent papers including:

  • Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA, 2021, IEEE Transactions on Dependable and Secure Computing
  • A novel approach for detecting vulnerable IoT devices connected behind a home NAT, 2020, Computers & Security
  • The Security of IP-Based Video Surveillance Systems, 2020, MDPI (MDPI AG)
  • Android malware detection via an app similarity graph, 2021, Computers & Security
  • TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack, 2022, IEEE Transactions on Information Forensics and Security

Frequent co-authors collaborating with Shabtai include:

  • Yuval Elovici (120 joint publications)
  • Ron Bitton (26 joint publications)
  • Dudu Mimran (21 joint publications)
  • Oleg Brodt (19 joint publications)
  • Rami Puzis (15 joint publications)

Their research outputs are often published in venues such as:

  • arXiv (Cornell University) with 79 publications
  • Computers & Security with 14 publications
  • Zenodo (CERN European Organization for Nuclear Research) with 9 publications
  • SSRN Electronic Journal with 7 publications
  • Sensors with 5 publications

Shabtai's academic focus demonstrates a sustained interest in cybersecurity challenges, particularly in detecting and mitigating threats in networked systems and developing resilient machine learning models against adversarial attacks. Their interdisciplinary approach integrates methods from artificial intelligence and signal processing to strengthen information security frameworks.

Best Publications

  • N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

    Yair Meidan;Michael Bohadana;Yael Mathov;Yisroel Mirsky

  • Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection.

    Yisroel Mirsky;Tomer Doitshman;Yuval Elovici;Asaf Shabtai

  • N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

    Yair Meidan;Michael Bohadana;Yael Mathov;Yisroel Mirsky

  • Andromaly: a behavioral malware detection framework for android devices

    Asaf Shabtai;Uri Kanonov;Yuval Elovici;Chanan Glezer

  • Google Android: A Comprehensive Security Assessment

    A. Shabtai;Y. Fledel;U. Kanonov;Y. Elovici

  • ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis

    Yair Meidan;Michael Bohadana;Asaf Shabtai;Juan David Guarnizo

  • Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

    Asaf Shabtai;Robert Moskovitch;Yuval Elovici;Chanan Glezer

  • Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

    Moshe Kravchik;Asaf Shabtai

  • Detecting unknown malicious code by applying classification techniques on OpCode patterns

    Asaf Shabtai;Robert Moskovitch;Clint Feher;Shlomi Dolev

  • A Survey of Data Leakage Detection and Prevention Solutions

    Asaf Shabtai;Yuval Elovici;Lior Rokach

  • Securing Android-Powered Mobile Devices Using SELinux

    Asaf Shabtai;Yuval Fledel;Yuval Elovici

  • Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

    Asaf Shabtai;Yuval Fledel;Yuval Elovici

  • Mobile malware detection through analysis of deviations in application network behavior

    Asaf Shabtai;Lena Tenenboim-Chekina;Dudu Mimran;Lior Rokach

  • Detection of Unauthorized IoT Devices Using Machine Learning Techniques

    Yair Meidan;Michael Bohadana;Asaf Shabtai;Martín Ochoa

  • Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

    Ishai Rosenberg;Asaf Shabtai;Yuval Elovici;Lior Rokach

  • Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

    Moshe Kravchik;Asaf Shabtai

  • Security Testbed for Internet-of-Things Devices

    Shachar Siboni;Vinay Sachidananda;Yair Meidan;Michael Bohadana

  • Improving malware detection by applying multi-inducer ensemble

    Eitan Menahem;Asaf Shabtai;Lior Rokach;Yuval Elovici

  • Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

    Ishai Rosenberg;Asaf Shabtai;Lior Rokach;Yuval Elovici

  • Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method

    Asaf Shabtai;Uri Kanonov;Yuval Elovici

  • Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

    Moshe Kravchik;Asaf Shabtai

Frequent Co-Authors

Yuval Elovici
Yuval Elovici Ben-Gurion University of the Negev
Lior Rokach
Lior Rokach Ben-Gurion University of the Negev
Bracha Shapira
Bracha Shapira Ben-Gurion University of the Negev
Robert Moskovitch
Robert Moskovitch Columbia University
Yuval Shahar
Yuval Shahar Ben-Gurion University of the Negev
Battista Biggio
Battista Biggio University of Cagliari
Shlomi Dolev
Shlomi Dolev Ben-Gurion University of the Negev
Antonio Puliafito
Antonio Puliafito University of Messina
Francesco Longo
Francesco Longo University of Messina
Nils Ole Tippenhauer
Nils Ole Tippenhauer Saarland University

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Related Online Degrees & Career Pathways

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Each of these pathways can be a valuable addition to your Computer Science education, helping you tailor your skills and expand your career possibilities.

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