World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10732
World Ranking
9988
National Ranking
1

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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in Computer Science opens doors to a variety of flexible degree options. Many learners choose cheap online college classes to save on tuition while building a solid foundation in technology. If your academic record isn’t perfect, don’t worry—there are high-quality online schools that accept low gpa, allowing you to pursue your career goals regardless of past grades.

Fast-track options are also available. For motivated students, accelerated computer science degree programs provide a way to complete coursework in less time and start your professional journey sooner. If you’re interested in diverse career opportunities, it’s worth noting how other fields, such as environmental science, offer broad options; see what can you do with an environmental science degree for a closer look.

Online pathways offer flexibility and accessibility, making it easier than ever to gain new skills and launch a rewarding technology career.

Best Scientists Citing Pavel Laskov

Trending Scientists