World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
12624
World Ranking
6355
National Ranking
298

Konrad Rieck 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 Konrad Rieck 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: 138 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Konrad Rieck 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 Konrad Rieck 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

Konrad Rieck is affiliated with Technische Universität Braunschweig in Germany and specializes in computer science with a focus on artificial intelligence, signal processing, and information systems. Their research spans several subfields including computer networks and communications, as well as hardware and architecture.

The primary topics of their work include advanced malware detection techniques, adversarial robustness in machine learning, network security and intrusion detection, software engineering research, anomaly detection techniques and applications, physical unclonable functions (PUFs) and hardware security, and cryptographic implementations and security.

Konrad Rieck has published extensively, with a significant portion of their work appearing in the following venues:

  • arXiv (Cornell University)
  • Annual Computer Security Applications Conference
  • Proceedings on Privacy Enhancing Technologies
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Security & Privacy

Recent publications attributed to or coauthored by Rieck include:

  • "Dos and Don'ts of Machine Learning in Computer Security," 2020, arXiv (Cornell University)
  • "Lessons Learned on Machine Learning for Computer Security," 2023, IEEE Security & Privacy
  • "Machine Unlearning of Features and Labels," 2021, arXiv (Cornell University)
  • "Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification," 2020, arXiv (Cornell University)
  • "Approximate kernels for trees," 2022, Fraunhofer-Publica (Fraunhofer-Gesellschaft)

Frequent collaborators indicating established research partnerships include:

  • Erwin Quiring
  • Daniel J. Arp
  • Christian Wressnegger
  • Alexander Warnecke
  • Lukas Pirch

Their body of work reflects engagement with topics at the intersection of machine learning techniques and computer security challenges, addressing both theoretical frameworks and applied methods.

Best Publications

  • DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

    Daniel Arp;Michael Spreitzenbarth;Malte Hubner;Hugo Gascon

  • Automatic analysis of malware behavior using machine learning

    Konrad Rieck;Philipp Trinius;Carsten Willems;Thorsten Holz_aff n

  • Learning and Classification of Malware Behavior

    Konrad Rieck;Thorsten Holz;Carsten Willems;Patrick Düssel

  • Modeling and Discovering Vulnerabilities with Code Property Graphs

    Fabian Yamaguchi;Nico Golde;Daniel Arp;Konrad Rieck

  • Measuring and Detecting Fast-Flux Service Networks

    Thorsten Holz;Christian Gorecki;Konrad Rieck;Felix C. Freiling

  • Toward supervised anomaly detection

    Nico Görnitz;Marius Kloft;Konrad Rieck;Ulf Brefeld

  • Structural detection of android malware using embedded call graphs

    Hugo Gascon;Fabian Yamaguchi;Daniel Arp;Konrad Rieck

  • Learning intrusion detection: supervised or unsupervised?

    Pavel Laskov;Patrick Düssel;Christin Schäfer;Konrad Rieck

  • Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

    Ambra Demontis;Marco Melis;Battista Biggio;Davide Maiorca

  • Cujo: efficient detection and prevention of drive-by-download attacks

    Konrad Rieck;Tammo Krueger;Andreas Dewald

  • Generalized vulnerability extrapolation using abstract syntax trees

    Fabian Yamaguchi;Markus Lottmann;Konrad Rieck

  • VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits

    Henning Perl;Sergej Dechand;Matthew Smith;Daniel Arp

  • Automatic Inference of Search Patterns for Taint-Style Vulnerabilities

    Fabian Yamaguchi;Alwin Maier;Hugo Gascon;Konrad Rieck

  • Chucky: exposing missing checks in source code for vulnerability discovery

    Fabian Yamaguchi;Christian Wressnegger;Hugo Gascon;Konrad Rieck

  • Vulnerability extrapolation: assisted discovery of vulnerabilities using machine learning

    Fabian Yamaguchi;Felix Lindner;Konrad Rieck

  • Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols

    Hugo Gascon;Christian Wressnegger;Fabian Yamaguchi;Daniel Arp

  • A method and apparatus for automatic comparison of data sequences

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

  • Continuous authentication on mobile devices by analysis of typing motion behavior

    Hugo Gascon;Sebastian Uellenbeck;Christopher Wolf;Konrad Rieck

  • Linear-Time Computation of Similarity Measures for Sequential Data

    Konrad Rieck;Pavel Laskov

  • Poisoning behavioral malware clustering

    Battista Biggio;Konrad Rieck;Davide Ariu;Christian Wressnegger

  • Intelligent data analysis: keeping pace with technological advances

    Xiaohui Liu

Frequent Co-Authors

Pavel Laskov
Pavel Laskov University of Liechtenstein
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Jean-Pierre Seifert
Jean-Pierre Seifert Technical University of Berlin
Thorsten Holz
Thorsten Holz Max Planck Institute for Security and Privacy
Giorgio Giacinto
Giorgio Giacinto University of Cagliari
Federico Maggi
Federico Maggi University of Sydney
Christopher Kruegel
Christopher Kruegel University of California, Santa Barbara
Fabio Roli
Fabio Roli University of Genoa
Battista Biggio
Battista Biggio University of Cagliari
Engin Kirda
Engin Kirda Northeastern University

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Related Online Degrees & Career Pathways

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