Pavel Laskov

发表

An Improved Decomposition Algorithm for Regression Support Vector Machines

Pavel Laskov, P. Laskov, 1999, NIPS.

A Self-learning System for Detection of Anomalous SIP Messages

Klaus-Robert Müller, Konrad Rieck, Stefan Wahl, 2008, IPTComm.

Language models for detection of unknown attacks in network traffic

Konrad Rieck, Pavel Laskov, 2006, Journal in Computer Virology.

Curvature-Based Algorithms for Nonrigid Motion and Correspondence Estimation

Chandra Kambhamettu, Pavel Laskov, P. Laskov, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

3D nonrigid motion analysis under small deformations

Dmitry B. Goldgof, Chandra Kambhamettu, Matthew He, 1995, Image Vis. Comput..

Static detection of malicious JavaScript-bearing PDF documents

Pavel Laskov, Nedim Srndic, P. Laskov, 2011, ACSAC '11.

Feasible Direction Decomposition Algorithms for Training Support Vector Machines

Pavel Laskov, P. Laskov, 2002, Machine Learning.

Hidost: a static machine-learning-based detector of malicious files

Pavel Laskov, Nedim Srndic, 2016, EURASIP J. Inf. Secur..

Visualization of anomaly detection using prediction sensitivity

Klaus-Robert Müller, Christin Schäfer, Konrad Rieck, 2005, Sicherheit.

Network attack detection and defense – Manifesto of the Dagstuhl Perspective Workshop, March 2nd–6th, 2008

Georg Carle, Christopher Krügel, Richard A. Kemmerer, 2009, Computer Science - Research and Development.

Distributed output encoding for multi-class pattern recognition

Richard A. Foulds, Pavel Laskov, David M. Saxe, 1999, Proceedings 10th International Conference on Image Analysis and Processing.

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

Klaus-Robert Müller, Christin Schäfer, Igor Kotenko, 2004 .

Proceedings of the Third international conference on Detection of Intrusions and Malware & Vulnerability Assessment

Pavel Laskov, Roland Büschkes, 2006 .

Detection of Malicious PDF Files Based on Hierarchical Document Structure

Pavel Laskov, Nedim Srndic, P. Laskov, 2013, NDSS.

TokDoc: a self-healing web application firewall

Konrad Rieck, Pavel Laskov, Tammo Krueger, 2010, SAC '10.

Sicherer Umgang mit sensiblen Daten - technische Prävention und Reaktionen auf Datenschutzverletzungen

Sebastian Pape, Pavel Laskov, Ulrich Greveler, 2009, GI Jahrestagung.

Botzilla: detecting the "phoning home" of malicious software

Konrad Rieck, Thorsten Holz, Guido Schwenk, 2010, SAC '10.

Efficient Algorithms for Similarity Measures over Sequential Data: A Look Beyond Kernels

Klaus-Robert Müller, Konrad Rieck, Pavel Laskov, 2006, DAGM-Symposium.

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Fabio Roli, Blaine Nelson, J. Doug Tygar, 2012, Dagstuhl Manifestos.

Online SVM learning: from classification to data description and back

David M. J. Tax, Pavel Laskov, D. Tax, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Machine learning in adversarial environments

Richard Lippmann, Pavel Laskov, R. Lippmann, 2010, Machine Learning.

Foreword

Eric Filiol, Pavel Laskov, Roland Büschkes, 2006, Journal in Computer Virology.

Evasion Attacks against Machine Learning at Test Time pdf

Fabio Roli, Blaine Nelson, Giorgio Giacinto, 2013, ECML/PKDD.

A factorization method for the classification of infrared spectra

Andreas Zell, Pavel Laskov, Carsten Henneges, 2010, BMC Bioinformatics.

Support Vector Machines Under Adversarial Label Noise

Blaine Nelson, Battista Biggio, Pavel Laskov, 2011, ACML.

Automatic feature selection for anomaly detection

Marius Kloft, Ulf Brefeld, Pavel Laskov, 2008, AISec '08.

CLIO - A Cross-Layer Information Service for Overlay Network Optimization

Ralph Holz, Pavel Laskov, Heiko Niedermayer, 2009, KiVS.

Securing IMS against novel threats

Klaus-Robert Müller, Konrad Rieck, Stefan Wahl, 2009 .

FPGA vs. Multi-core CPUs vs. GPUs: Hands-On Experience with a Sorting Application

Cristian Grozea, Pavel Laskov, Zorana Bankovic, 2010, Facing the Multicore-Challenge.

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

Sören Sonnenburg, Konrad Rieck, Pavel Laskov, 2006, NIPS.

Call for Papers - Detection of Intrusions & Malware and Vulnerability Assessment (DIMVA 2006)

Pavel Laskov, Roland Büschkes, Marc Heuse, 2007 .

Understanding the risk factors of learning in adversarial environments

Blaine Nelson, Battista Biggio, Pavel Laskov, 2011, AISec '11.

Poisoning Attacks against Support Vector Machines

Blaine Nelson, Battista Biggio, Pavel Laskov, 2012, ICML.

Online Anomaly Detection under Adversarial Impact

Marius Kloft, Pavel Laskov, M. Kloft, 2010, AISTATS.

Learning and Classification of Malware Behavior

Carsten Willems, Konrad Rieck, Thorsten Holz, 2008, DIMVA.

Machine Learning for Intrusion Detection

Klaus-Robert Müller, Konrad Rieck, Pavel Laskov, 2007, NATO ASI Mining Massive Data Sets for Security.

Cyber-Critical Infrastructure Protection Using Real-Time Payload-Based Anomaly Detection

Christof Störmann, Pavel Laskov, Patrick Düssel, 2009, CRITIS.

Incremental Support Vector Learning: Analysis, Implementation and Applications

Klaus-Robert Müller, Pavel Laskov, Stefan Krüger, 2006, J. Mach. Learn. Res..

Incorporation of Application Layer Protocol Syntax into Anomaly Detection

Konrad Rieck, Pavel Laskov, Patrick Düssel, 2008, ICISS.

Comparison of 3D Algorithms for Non-rigid Motion and Correspondence Estimation

Chandra Kambhamettu, Pavel Laskov, 2001, BMVC.

Using classification to determine the number of finger strokes on a multi-touch tactile device

Pavel Laskov, Caspar von Wrede, P. Laskov, 2004, ESANN.

08102 Manifesto -- Perspectives Workshop: Network Attack Detection and Defense

Georg Carle, Christopher Krügel, Richard A. Kemmerer, 2008, Network Attack Detection and Defense.

Detection of Intrusions and Malware, and Vulnerability Assessment: 19th International Conference, DIMVA 2022, Cagliari, Italy, June 29 –July 1, 2022, Proceedings

Pavel Laskov, Roland Büschkes, P. Laskov, 2022, International Conference on Detection of intrusions and malware, and vulnerability assessment.

A MULTI-STAGE APPROACH TO FINGERSPELLING AND GESTURE RECOGNITION

Pavel Laskov, Roman Erenshteyn, 1996 .

Linear-Time Computation of Similarity Measures for Sequential Data

Konrad Rieck, Pavel Laskov, K. Rieck, 2008, J. Mach. Learn. Res..

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Fabio Roli, Blaine Nelson, J. Doug Tygar, 2012, Dagstuhl Reports.

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

Andreas Ziehe, Klaus-Robert Müller, Guido Nolte, 2004, J. Mach. Learn. Res..

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

Pavel Laskov, Nedim Srndic, P. Laskov, 2014, 2014 IEEE Symposium on Security and Privacy.

Security analysis of online centroid anomaly detection

Marius Kloft, Pavel Laskov, M. Kloft, 2010, J. Mach. Learn. Res..

Efficient and Accurate Lp-Norm Multiple Kernel Learning

Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg, 2009, NIPS.

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

Pavel Laskov, Roland Büschkes, Dimva, 2006, DIMVA.

Detecting Unknown Network Attacks Using Language Models

Konrad Rieck, Pavel Laskov, K. Rieck, 2006, DIMVA.

Stopping conditions for exact computation of leave-one-out error in support vector machines

Klaus-Robert Müller, Pavel Laskov, Vojtech Franc, 2008, ICML '08.

Classifier Evasion and Adversarial Games

Blaine Nelson, Pavel Laskov, Blaine Nelson, 2012 .

Recognition approach to gesture language understanding

Richard A. Foulds, Pavel Laskov, Roman Erenshteyn, 1996, Proceedings of 13th International Conference on Pattern Recognition.

Learning Intrusion Detection: Supervised or Unsupervised?

Christin Schäfer, Konrad Rieck, Pavel Laskov, 2005, ICIAP.

Anomaly Detection at “supersonic” Speed

Cristian Grozea, Pavel Laskov, P. Laskov, 2012, it Inf. Technol..

Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers

Blaine Nelson, Battista Biggio, Pavel Laskov, 2011, ACML.

A framework for quantitative security analysis of machine learning

Marius Kloft, Pavel Laskov, M. Kloft, 2009, AISec '09.

Securing IMS against novel threats

Klaus-Robert Müller, Konrad Rieck, Stefan Wahl, 2009, Bell Labs Technical Journal.

Visualization and Explanation of Payload-Based Anomaly Detection

Konrad Rieck, Pavel Laskov, 2009, 2009 European Conference on Computer Network Defense.

Detection of illicit cryptomining using network metadata

Pavel Laskov, Michele Russo, Nedim Šrndić, 2021 .

A fast algorithm for joint diagonalization with application to blind source separation – BLISS Technical Report ∗ –

Andreas Ziehe, Klaus-Robert Müller, Guido Nolte, 2003 .