P. Laskov

发表

A LINEAR LEAST-SQUARES ALGORITHM FOR JOINT DIAGONALIZATION

A. Ziehe, P. Laskov, 2003 .

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

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

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

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

Non-sparse Multiple Kernel Learning

M. Kloft, Ulf Brefeld, S. Sonnenburg, 2008 .

Efficient and Accurate Lp-Norm Multiple Kernel Learning

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

Evasion Attacks against Machine Learning at Test Time pdf

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

An Improved Decomposition Algorithm for Regression Support Vector Machines

Pavel Laskov, P. Laskov, 1999, NIPS.

Support Vector Machines Under Adversarial Label Noise

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

Poisoning Attacks against Support Vector Machines

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

Detection of Malicious PDF Files Based on Hierarchical Document Structure

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

Static detection of malicious JavaScript-bearing PDF documents

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

Learning and Classification of Malware Behavior

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

Linear-Time Computation of Similarity Measures for Sequential Data

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

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

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

2 Machine Learning Methods for Computer Security 1 Executive Summary

F. Roli, A. Joseph, Blaine Nelson, 2013 .

Machine Learning Methods for Computer Security (dagstuhl Perspectives Workshop 12371) Machine Learning Methods for Computer Security Machine Learning Methods for Computer Security 1 Executive Summary Table of Contents

J. D. Tygar, B. Schiele, F. Roli, 2022 .

Extensions of Differential-Geometric Algorithms for Estimation of 3D Non-Rigid Motion and Corresp

P. Laskov, 2001 .

Kernel Method for Linear Operator Variational Problems : Non-linear Unit Normal Algorithm for Non-Rigid Motion Estimation of 3 D Surfaces

P. Laskov, C. Kambhamettu, 2022 .

Machine learning in adversarial environments

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

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

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

FIPS : FIRST Intrusion Prevention System

K. Rieck, C. Gehl, P. Laskov, 2010 .

Learning from network traffic: computing kernels for connection content

K. Rieck, P. Laskov, 2005 .

Towards Understanding the Skill Gap in Cybersecurity

P. Laskov, Frédéric Thiesse, M. Felderer, 2022, 2204.13793.

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

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

Müller Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Igor Kotenko, P. Laskov, C. Schäfer, 2007 .

A factorization method for the classification of infrared spectra

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

Detection of illicit cryptomining using network metadata

Pavel Laskov, Michele Russo, Nedim Šrndić, 2021 .

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.

Classifier Evasion and Adversarial Games

Blaine Nelson, Pavel Laskov, Blaine Nelson, 2012 .

SoK: The Impact of Unlabelled Data in Cyberthreat Detection

P. Laskov, Giovanni Apruzzese, A.T. Tastemirova, 2022, 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P).

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

P. Laskov, Giovanni Apruzzese, A.T. Tastemirova, 2022, 2207.01531.

The Role of Machine Learning in Cybersecurity

Edgardo Montes de Oca, Luis Antonio Búrdalo Rapa, Athanasios Vasileios Grammatopoulos, 2022, Digital Threats: Research and Practice.

Learning Intrusion Detection: Supervised or Unsupervised?

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

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

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

Feasible Direction Decomposition Algorithms for Training Support Vector Machines

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

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

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

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..

Incremental Support Vector Learning: Analysis, Implementation and Applications

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

Security analysis of online centroid anomaly detection

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

Online Anomaly Detection under Adversarial Impact

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

Securing IMS against novel threats

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

A Self-learning System for Detection of Anomalous SIP Messages

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

Machine Learning for Intrusion Detection

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

Detecting Unknown Network Attacks Using Language Models

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

Anomaly Detection at “supersonic” Speed

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

Botzilla: detecting the "phoning home" of malicious software

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

TokDoc: a self-healing web application firewall

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

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

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

Incorporation of Application Layer Protocol Syntax into Anomaly Detection

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

Visualization of anomaly detection using prediction sensitivity

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

A framework for quantitative security analysis of machine learning

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

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

P. Laskov, Giovanni Apruzzese, A.T. Tastemirova, 2022, IEEE Transactions on Network and Service Management.

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

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

Automatic feature selection for anomaly detection

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

3D nonrigid motion analysis under small deformations

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

Curvature-Based Algorithms for Nonrigid Motion and Correspondence Estimation

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

Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers

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

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).

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

P. Laskov, Giovanni Apruzzese, J. Schneider, 2023, 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P).

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

P. Laskov, C. Schäfer, I. Kotenko, 2004, Prax. Inf.verarb. Kommun..

Detection of illicit cryptomining using network metadata

P. Laskov, Nedim Srndic, Michele Russo, 2021, EURASIP Journal on Information Security.

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.

Recognition approach to gesture language understanding

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

Understanding the risk factors of learning in adversarial environments

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

Non-sparse Multiple Kernel Learning

M. Kloft, Ulf Brefeld, S. Sonnenburg, 2008 .