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Computer Science

D-Index
47
Citations
12624
World Ranking
6354
National Ranking
297

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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