Recognition approach to gesture language understanding. - Roman Erenshteyn; Pavel Laskov; Richard A. Foulds; Lynn Messing; Garland Stern (ICPR, 1996)

Distributed Output Encoding for Multi-Class Pattern Recognition. - Roman Erenshteyn; Pavel Laskov; David M. Saxe; Richard A. Foulds (ICIAP, 1999)

An Improved Decomposition Algorithm for Regression Support Vector Machines. - Pavel Laskov (NIPS, 1999)

Comparison of 3D Algorithms for Non-rigid Motion and Correspondence Estimation. - Pavel Laskov; Chandra Kambhamettu (BMVC, 2001)

Feasible Direction Decomposition Algorithms for Training Support Vector Machines. - Pavel Laskov (Mach. Learn., 2002)

Online SVM learning: from classification to data description and back. - David M. J. Tax; Pavel Laskov (NNSP, 2003)

3D nonrigid motion analysis under small deformations. - Chandra Kambhamettu; Dmitry B. Goldgof; Matthew He; Pavel Laskov (Image Vis. Comput., 2003)

Curvature-Based Algorithms for Nonrigid Motion and Correspondence Estimation. - Pavel Laskov; Chandra Kambhamettu (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines. - Pavel Laskov; Christin Schäfer; Igor V. Kotenko (DIMVA, 2004)

Using classification to determine the number of finger strokes on a multi-touch tactile device. - Caspar von Wrede; Pavel Laskov (ESANN, 2004)

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 (J. Mach. Learn. Res., 2004)

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines. - Pavel Laskov; Christin Schäfer; Igor V. Kotenko; Klaus-Robert Müller (Prax. Inf.verarb. Kommun., 2004)

Learning Intrusion Detection: Supervised or Unsupervised?. - Pavel Laskov; Patrick Düssel; Christin Schäfer; Konrad Rieck (ICIAP, 2005)

Visualization of anomaly detection using prediction sensitivity. - Pavel Laskov; Konrad Rieck; Christin Schäfer; Klaus-Robert Müller (Sicherheit, 2005)

Efficient Algorithms for Similarity Measures over Sequential Data: A Look Beyond Kernels. - Konrad Rieck; Pavel Laskov; Klaus-Robert Müller (DAGM-Symposium, 2006)

Detecting Unknown Network Attacks Using Language Models. - Konrad Rieck; Pavel Laskov (DIMVA, 2006)

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees. - Konrad Rieck; Pavel Laskov; Sören Sonnenburg (NIPS, 2006)

Incremental Support Vector Learning: Analysis, Implementation and Applications. - Pavel Laskov; Christian Gehl; Stefan Krüger; Klaus-Robert Müller (J. Mach. Learn. Res., 2006)

Machine Learning for Intrusion Detection. - Pavel Laskov; Konrad Rieck; Klaus-Robert Müller (NATO ASI Mining Massive Data Sets for Security, 2007)

Foreword. - Eric Filiol; Roland Büschkes; Pavel Laskov (J. Comput. Virol., 2007)

Language models for detection of unknown attacks in network traffic. - Konrad Rieck; Pavel Laskov (J. Comput. Virol., 2007)

Automatic feature selection for anomaly detection. - Marius Kloft; Ulf Brefeld; Patrick Düssel; Christian Gehl; Pavel Laskov (AISec, 2008)

08102 Manifesto – Perspectives Workshop: Network Attack Detection and Defense. - Georg Carle; Falko Dressler; Richard A. Kemmerer; Hartmut König; Christopher Kruegel; Pavel Laskov (Network Attack Detection and Defense, 2008)

Learning and Classification of Malware Behavior. - Konrad Rieck; Thorsten Holz; Carsten Willems; Patrick Düssel; Pavel Laskov (DIMVA, 2008)

Incorporation of Application Layer Protocol Syntax into Anomaly Detection. - Patrick Düssel; Christian Gehl; Pavel Laskov; Konrad Rieck (ICISS, 2008)

Stopping conditions for exact computation of leave-one-out error in support vector machines. - Vojtech Franc; Pavel Laskov; Klaus-Robert Müller (ICML, 2008)

A Self-learning System for Detection of Anomalous SIP Messages. - Konrad Rieck; Stefan Wahl; Pavel Laskov; Peter Domschitz; Klaus-Robert Müller (IPTComm, 2008)

Linear-Time Computation of Similarity Measures for Sequential Data. - Konrad Rieck; Pavel Laskov (J. Mach. Learn. Res., 2008)

A framework for quantitative security analysis of machine learning. - Pavel Laskov; Marius Kloft (AISec, 2009)

Cyber-Critical Infrastructure Protection Using Real-Time Payload-Based Anomaly Detection. - Patrick Düssel; Christian Gehl; Pavel Laskov; Jens-Uwe Bußer; Christof Störmann; Jan Kästner (CRITIS, 2009)

Sicherer Umgang mit sensiblen Daten - technische Prävention und Reaktionen auf Datenschutzverletzungen. - Ulrich Greveler; Pavel Laskov; Sebastian Pape 001 (GI Jahrestagung, 2009)

CLIO - A Cross-Layer Information Service for Overlay Network Optimization. - Dirk Haage; Ralph Holz; Heiko Niedermayer; Pavel Laskov (KiVS, 2009)

Efficient and Accurate Lp-Norm Multiple Kernel Learning. - Marius Kloft; Ulf Brefeld; Sören Sonnenburg; Pavel Laskov; Klaus-Robert Müller; Alexander Zien (NIPS, 2009)

Securing IMS against novel threats. - Stefan Wahl; Konrad Rieck; Pavel Laskov; Peter Domschitz; Klaus-Robert Müller (Bell Labs Tech. J., 2009)

Network attack detection and defense - Manifesto of the Dagstuhl Perspective Workshop, March 2nd-6th, 2008. - Georg Carle; Falko Dressler; Richard A. Kemmerer; Hartmut König; Christopher Kruegel; Pavel Laskov (Comput. Sci. Res. Dev., 2009)

FPGA vs. Multi-core CPUs vs. GPUs: Hands-On Experience with a Sorting Application. - Cristian Grozea; Zorana Bankovic; Pavel Laskov (Facing the Multicore-Challenge, 2010)

TokDoc: a self-healing web application firewall. - Tammo Krueger; Christian Gehl; Konrad Rieck; Pavel Laskov (SAC, 2010)

Botzilla: detecting the "phoning home" of malicious software. - Konrad Rieck; Guido Schwenk; Tobias Limmer; Thorsten Holz; Pavel Laskov (SAC, 2010)

A factorization method for the classification of infrared spectra. - Carsten Henneges; Pavel Laskov; Endang Darmawan; Juergen Backhaus; Bernd Kammerer; Andreas Zell (BMC Bioinform., 2010)

Online Anomaly Detection under Adversarial Impact. - Marius Kloft; Pavel Laskov (AISTATS, 2010)

Machine learning in adversarial environments. - Pavel Laskov; Richard Lippmann (Mach. Learn., 2010)

Static detection of malicious JavaScript-bearing PDF documents. - Pavel Laskov; Nedim Srndic (ACSAC, 2011)

Understanding the risk factors of learning in adversarial environments. - Blaine Nelson; Battista Biggio; Pavel Laskov (AISec, 2011)

Support Vector Machines Under Adversarial Label Noise. - Battista Biggio; Blaine Nelson; Pavel Laskov (ACML, 2011)

Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers. - Blaine Nelson; Battista Biggio; Pavel Laskov (ACML, 2011)

Poisoning Attacks against Support Vector Machines. - Battista Biggio; Blaine Nelson; Pavel Laskov (ICML, 2012)

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371). - Anthony D. Joseph; Pavel Laskov; Fabio Roli; J. Doug Tygar; Blaine Nelson (Dagstuhl Reports, 2012)

Anomaly Detection at "supersonic" Speed. - Cristian Grozea; Pavel Laskov (it Inf. Technol., 2012)

Security analysis of online centroid anomaly detection. - Marius Kloft; Pavel Laskov (J. Mach. Learn. Res., 2012)

Detection of Malicious PDF Files Based on Hierarchical Document Structure. - Nedim Srndic; Pavel Laskov (NDSS, 2013)

Evasion Attacks against Machine Learning at Test Time. - Battista Biggio; Igino Corona; Davide Maiorca; Blaine Nelson; Nedim Srndic; Pavel Laskov; Giorgio Giacinto; Fabio Roli (ECML/PKDD (3), 2013)

Practical Evasion of a Learning-Based Classifier: A Case Study. - Nedim Srndic; Pavel Laskov (IEEE Symposium on Security and Privacy, 2014)

Hidost: a static machine-learning-based detector of malicious files. - Nedim Srndic; Pavel Laskov (EURASIP J. Inf. Secur., 2016)

Detection of illicit cryptomining using network metadata. - Michele Russo; Nedim Srndic; Pavel Laskov (EURASIP J. Inf. Secur., 2021)

SoK: The Impact of Unlabelled Data in Cyberthreat Detection. - Giovanni Apruzzese; Pavel Laskov; Aliya Tastemirova (EuroS&P, 2022)

Towards Understanding the Skill Gap in Cybersecurity. - François Goupil; Pavel Laskov; Irdin Pekaric; Michael Felderer; Alexander Dürr; Frédéric Thiesse (ITiCSE (1), 2022)

Security of Machine Learning (Dagstuhl Seminar 22281). - Battista Biggio; Nicholas Carlini; Pavel Laskov; Konrad Rieck; Antonio Emanuele Cinà (Dagstuhl Reports, 2022)

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. - Giovanni Apruzzese; Rodion Vladimirov; Aliya Tastemirova; Pavel Laskov (IEEE Trans. Netw. Serv. Manag., 2022)

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. - Giovanni Apruzzese; Pavel Laskov; Johannes Schneider 002 (EuroS&P, 2023)

Network Attack Detection and Defense - AI-Powered Threats and Responses (Dagstuhl Seminar 23431). - Sven Dietrich; Frank Kargl; Hartmut König; Pavel Laskov; Artur Hermann (Dagstuhl Reports, 2023)

The Role of Machine Learning in Cybersecurity. - Giovanni Apruzzese; Pavel Laskov; Edgardo Montes de Oca; Wissam Mallouli; Luis Burdalo Rapa; Athanasios Vasileios Grammatopoulos; Fabio Di Franco (DTRAP, 2023)

Emerging Security Challenges of Large Language Models. - Hervé Debar; Sven Dietrich; Pavel Laskov; Emil C. Lupu; Eirini Ntoutsi (CoRR, 2024)

The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning. - Advije Rizvani; Giovanni Apruzzese; Pavel Laskov (CODASPY, 2025)

SoK: On the Offensive Potential of AI. - Saskia Laura Schröer; Giovanni Apruzzese; Soheil Human; Pavel Laskov; Hyrum S. Anderson; Edward W. N. Bernroider; Aurore Fass; Ben Nassi; Vera Rimmer; Fabio Roli; Samer Salam; Chi En Ashley Shen; et al. (SaTML, 2025)

The Dark Side of the Web: Towards Understanding Various Data Sources in Cyber Threat Intelligence. - Saskia Laura Schröer; Noé Canevascini; Irdin Pekaric; Philine Widmer; Pavel Laskov (CoRR, 2025)