World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10732
World Ranking
9986
National Ranking
1

Pavel Laskov 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 Pavel Laskov 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: 83 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Pavel Laskov 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 Pavel Laskov 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Pavel Laskov is affiliated with the University of Liechtenstein in Liechtenstein. Their research primarily spans the domain of Computer Science, with particular contributions in related subfields such as Information Systems, Signal Processing, Computer Networks and Communications, Artificial Intelligence, and Hardware and Architecture.

The scientist's work extensively covers topics within cybersecurity and machine learning. Key research areas include:

  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection
  • Information and Cyber Security
  • Internet Traffic Analysis and Secure E-voting
  • Adversarial Robustness in Machine Learning
  • Physical Unclonable Functions (PUFs) and Hardware Security
  • Software Engineering Research

Pavel Laskov has published in several venues, with a focus on peer-reviewed journals and open-access repositories. Frequent publication venues include:

  • arXiv (Cornell University)
  • Digital Threats Research and Practice
  • IEEE Transactions on Network and Service Management
  • EURASIP Journal on Information Security

Representative recent papers illustrate their research scope and thematic interests:

  • The Role of Machine Learning in Cybersecurity, 2022, Digital Threats Research and Practice
  • Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples, 2022, IEEE Transactions on Network and Service Management
  • Detection of Illicit Cryptomining Using Network Metadata, 2021, EURASIP Journal on Information Security
  • Towards Understanding the Skill Gap in Cybersecurity, 2022, arXiv (Cornell University)
  • SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection, 2023, arXiv (Cornell University)

The scientist has collaborated regularly with other researchers, including notable coauthors such as:

  • Giovanni Apruzzese
  • Saskia Laura Schröer
  • Edgardo Montes de
  • Wissam Mallouli
  • Luis Brdalo Rapa

This profile indicates a research career characterized by interdisciplinary work linking cybersecurity challenges with machine learning approaches, supported by a consistent publication record across well-regarded academic venues. Pavel Laskov's work engages with both theoretical and applied aspects of information security and network management.

Best Publications

  • Evasion attacks against machine learning at test time

    Battista Biggio;Igino Corona;Davide Maiorca;Blaine Nelson

  • Poisoning Attacks against Support Vector Machines

    Battista Biggio;Blaine Nelson;Pavel Laskov

  • Learning and Classification of Malware Behavior

    Konrad Rieck;Thorsten Holz;Carsten Willems;Patrick Düssel

  • Evasion Attacks against Machine Learning at Test Time

    Battista Biggio;Igino Corona;Davide Maiorca;Blaine Nelson

  • Incremental Support Vector Learning: Analysis, Implementation and Applications

    Pavel Laskov;Christian Gehl;Stefan Krüger;Klaus-Robert Müller;Klaus-Robert Müller

  • Practical Evasion of a Learning-Based Classifier: A Case Study

    Nedim rndic;Pavel Laskov

  • Support Vector Machines Under Adversarial Label Noise

    Battista Biggio;Blaine Nelson;Pavel Laskov

  • Learning intrusion detection: supervised or unsupervised?

    Pavel Laskov;Patrick Düssel;Christin Schäfer;Konrad Rieck

  • Efficient and Accurate Lp-Norm Multiple Kernel Learning

    Marius Kloft;Ulf Brefeld;Pavel Laskov;Klaus-Robert Müller

  • A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation

    Andreas Ziehe;Pavel Laskov;Guido Nolte;Klaus-Robert Müller;Klaus-Robert Müller

  • Static detection of malicious JavaScript-bearing PDF documents

    Pavel Laskov;Nedim Šrndić

  • Detection of Intrusions and Malware, and Vulnerability Assessment

    Roland Büschkes;Pavel Laskov

  • The Role of Machine Learning in Cybersecurity

    Unknown

  • Detection of Malicious PDF Files Based on Hierarchical Document Structure.

    Nedim Srndic;Pavel Laskov

  • Detection of Intrusions and Malware & Vulnerability Assessment, Third International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings

    Unknown

  • Online Anomaly Detection under Adversarial Impact

    Marius Kloft;Pavel Laskov

  • Online SVM learning: from classification to data description and back

    D.M.J. Tax;P. Laskov

  • A method and apparatus for automatic comparison of data sequences

    Konrad Rieck;Pavel Laskov;Klaus-Robert Müller;Patrick Düssel

  • Linear-Time Computation of Similarity Measures for Sequential Data

    Konrad Rieck;Pavel Laskov

  • Machine learning in adversarial environments

    Pavel Laskov;Richard Lippmann

  • Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines

    Pavel Laskov;Christin Schäfer;Igor V. Kotenko;Klaus-Robert Müller

  • Language models for detection of unknown attacks in network traffic

    Konrad Rieck;Pavel Laskov

  • Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines.

    Pavel Laskov;Christin Schäfer;Igor V. Kotenko

Frequent Co-Authors

Konrad Rieck
Konrad Rieck Technische Universität Braunschweig
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Marius Kloft
Marius Kloft Technical University of Kaiserslautern
Battista Biggio
Battista Biggio University of Cagliari
Georg Carle
Georg Carle Technical University of Munich
Fabio Roli
Fabio Roli University of Genoa
Christopher Kruegel
Christopher Kruegel University of California, Santa Barbara
Richard A. Kemmerer
Richard A. Kemmerer University of California, Santa Barbara
Falko Dressler
Falko Dressler Technical University of Berlin
Chandra Kambhamettu
Chandra Kambhamettu University of Delaware

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