HomeWho 's who

Prof. Dr. Pavel Laskov

Chair
Hilti Chair for Data and Application Security
Current Activity
Pavel Laskov is full professor at the Institute of Information Systems and head of the Hilti Chair of Daata and Application Security. His research is focused on the development of reactive security mechanisms, especially for detection of and response to novel attacks. Such mechanisms are crucial for data-centric applications such as autonomous systems, cryptocurrencies and Internet-of-Things.

Several research assistant positions are currently open at the Chair. If you are interested in pursuing Ph.D. in the field of information security please contact me directly.
Education
1996 — 2001

University of Delaware (USA), PhD in Computer Science

1994 — 1996

University of Delaware (USA), MSc in Computer Science

1988 — 1996

Moscow Institute of Radio, Electronics and Automation, Diploma in Computer and Systems Engineering

Career
since 2018

University of Liechtenstein, Full Professor. Head of Hilti Chair of Data and Application Security

2014 — 2018

Huawei European Research Center (Munich), Principal Engineer, Head of "Big Data Threat Analysis" Team

2009 — 2016

University of Tübingen, Heisenberg Fellow

2001 — 2009

Fraunhofer Institute for Computer Architecture and Software Technology (Berlin), Project and research group head

1996 — 2001

University of Delaware (USA), Research and teachning assistant

1996

AT&T Research (USA), Summer Intern

Portrait
Detection of Malicious Cryptomining in Network Metadata
FFF-Förderprojekt, December 2018 until December 2019

Cryptocurrencies and related blockchain technologies are one of the most fascinating developments in information technology in the last decade. In 2017, the cryptocurrency market hit an all-time high ... more ...

Detection of security threats through NetFlow analysis
Preproposal PhD-Thesis, since September 2018

Michele's research deals with the detection of security threats through aggregate network information, specifically the Cisco's NetFlow format. The latter provides a summary of the communications in ... more ...

  • Srndic, N., & Laskov, P. (2016). Hidost: a static machine-learning-based malicious software detector for multiple formats. EURASIP Journal on Information Security, 2016(22).

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  • Grozea, C., & Laskov, P. (2012). Anomaly detection at "supersonic" speed. Information Technology, 54(2), 82-89.

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  • Kloft, M., & Laskov, P. (2012). Security analysis of online centroid anomaly detection. Journal of Machine Learning Research, 13(1), 3133-3176.

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  • Henneges, C., Laskov, P., Darmawan, E., Backhaus, J., Kammerer, B., & Zell, A. (2010). A factorization method for the classication of infrared spectra. BMC Bioinformatics, 11(1), 561-572.

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  • Wahl, S., Rieck, K., Domschitz, P., Laskov, P., & Müller, K.-R. (2009). Securing MMoIP systems against novel threats. Bell Labs Technical Journal, 14(1), 243-257.

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  • Rieck, K., & Laskov, P. (2008). Linear-time computation of similarity measures for sequential data. Journal of Machine Learning Research, 9, 23-48.

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  • Rieck, K., & Laskov, P. (2007). Language models for detection of unknown attacks in network traffic. Journal in Computer Virology, 2(4), 243-256.

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  • Laskov, P., Gehl, C., Krüger, S., & Müller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909-1936.

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  • Laskov, P., Schäfer, C., Kotenko, I., & Müller, K.-R. (2004). Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines. Praxis der Informationsverarbeitung und Kommunikation, 27(4), 228-236.

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  • Ziehe, A., Laskov, P., Notel, G., & Müller, K.-R. (2004). A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation. Journal of Machine Learning Research, 5, 777-800.

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  • Kambhamettu, C., Goldgof, D., He, M., & Laskov, P. (2003). 3D nonrigid motion analysis under small deformations. Image and Vision Computing, 21(3), 229-245.

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  • Laskov, P., & Kambhamettu, C. (2003). Curvature-based algorithms for non-rigid motion and correspondence estimation. IEEE Transations on Pattern Analysis and Machine Intelligence, 25(10), 1349-1354.

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  • Laskov, P. (2002). Feasible direction decomposition algorithms for training support vector machines. Machine Learning, 46(1-3), 315-349.

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  • Büschkes, R., & Laskov, P. (2006). Detection of Intrusions and Malware & Vul- nerability Assessment : Springer.

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  • Laskov, P., Rieck, K., & Müller, K.-R. (2008). Machine learning for intrusion detection. In Mining Massive Data Sets for Security (pp. 366-373): IOS Press.

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  • Mika, S., Schäfer, C., Laskov, P., Tax, D., & Müller, K.-R. (2004). Handbook of Computational Statistics. In ch. Support Vector Machines (pp. 841-876): Springer.

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  • Srndic, N., & Laskov, P. (2014). Practical evasion of a learning-based classier: A case study. Paper presented at the IEEE Symposium on Security and Privacy.

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  • Biggio, B., Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., & Roli, F. (2013). Evasion attacks against machine learning at test time. Paper presented at the European Conference on Machine Learning.

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  • Srndic, N., & Laskov, P. (2013). Detection of malicious PDF files based on hierarchical document structure. Paper presented at the 20th Network and Distributed Systems Symposium.

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  • Biggio, B., Nelson, B., & Laskov, P. (2012). Poisoning attacks against support vector machines. Paper presented at the International Conference on Machine Learning.

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  • Biggio, B., Nelson, B., & Laskov, P. (2011). Support vector machines under adversarial label noise. Paper presented at the Asian Conference on Machine Learning.

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  • Laskov, P., & Srndic, N. (2011). Static detection of malicious JavaScript-bearing PDF documents. Paper presented at the Annual Computer Security Applications Conference.

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  • Nelson, B., Biggio, B., & Laskov, P. (2011). Microbagging estimators: An ensemble approach to distance-weighted classiers. Paper presented at the Asian Conference on Machine Learning.

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  • Nelson, B., Biggio, B., & Laskov, P. (2011). Understanding the risk factors of learning in adversarial environments. Paper presented at the ACM Workshop on Artificial Intelligence and Security.

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  • Grozea, C., Bankovic, Z., & Laskov, P. (2010). FPGA vs. multi-core CPUs vs. GPUs: Hands-on experience with sorting. Paper presented at the Facing the Multi-Core Challenge: Conference for Young Scientists, Heidelberger Akademie der Wissenschaften.

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  • Kloft, M., Brefeld, U., Sonnenburg, S., Laskov, P., Müller, K.-R., & Zien, A. (2010). Ecient and accurate Lp-Norm MKL.. Paper presented at the Advances in Neural Information Processing Systems 21.

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  • Klkoft, M., & Laskov, P. (2010). Online anomaly detection under adversarial impact. Paper presented at the Proceedings of the International Conference on Articial Intelligence and Statistics.

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  • Krüger, T., Gehl, C., Rieck, K., & Laskov, P. (2010). TokDoc: A self-healing web application firewall. Paper presented at the ACM Symposium on Applied Computing.

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  • Rieck, K., Schwenk, G., Limmer, T., Holz, T., & Laskov, P. (2010). Botzilla: Detecting the "phoning home" of malicious software. Paper presented at the ACM Symposium on Applied Computing.

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  • Düssel, P., Gehl, C., Laskov, P., Störmann, C., Busser, J.-U., & Kästner, J. (2009). Cyber-critical infrastructure protection using real-time payload-based anomaly detection. Paper presented at the Workshop on Critical Information Infrastructures Security.

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  • Haage, D., Holz, R., Nidermayer, H., & Laskov, P. (2009). CLIO - A cross-layer information service for overlay network optimization. Paper presented at the Kommunikation in Verteilten Systemen, KiVS 2009, 16. Fachtagung.

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  • Laskov, P., & Kloft, M. (2009). A framework for quantitative security analysis of machine learning. Paper presented at the ACM Workshop on Articial Intelligence and Security.

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  • Rieck, K., & Laskov, P. (2009). Visualization and explanation of payload-based anomaly detection. Paper presented at the European Conference on Computer Network Defense.

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  • Düssel, P., Gehl, C., Laskov, P., & Rieck, K. (2008). Incorporation of application layer protocol syntax into anomaly detection. Paper presented at the International Conference on Information Systems Security.

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  • Franc, V., Laskov, P., & Müller, K.-R. (2008). Stopping conditions for exact compu- tation of leave-one-out error in support vector machines.. , Interna tional Conference on Machine Learning.

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  • Kloft, M., Brefeld, U., Düssel, P., Gehl, C., & Laskov, P. (2008). Automatic fea ture selection for anomaly detection. Paper presented at the ACMWorkshop on Articial Intelligence and Security.

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  • Krüger, T., Gehl, C., Rieck, K., & Laskov, P. (2008). An architecture for inline anomaly detection. Paper presented at the European Conference on Computer Network Defense.

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  • Rieck, K., Holz, T., Willems, K., Düssel, P., & Laskov, P. (2008). Learning and classication of malware behavior. Paper presented at the Detection of Intrusions and Malware & Vulnera- bility Assessment.

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  • Rieck, K., Wahl, S., Laskov, P., Domschitz, P., & Müller, K.-R. (2008). Self- learning system for detection of anomalous SIP messages. Paper presented at the Principles, Systems and Applications of IP Telecommunications.

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  • Rieck, K., Laskov, P., & Sonnenburg, S. (2007). Computation of similarity measures for sequential data using generalized sux trees. Paper presented at the Advances in Neural Information Processing Systems.

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  • Rieck, K., & Laskov, P. (2006). Detecting unknown network attacks using language models. Paper presented at the Detection of Intrusions and Malware & Vulnerability Assessment.

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  • Rieck, K., Laskov, P., & Müller, K.-R. (2006). Efficient algorithms for similarity measures over sequential data: A look beyond kernels. Paper presented at the DAGM Symposium on Pattern Recognition.

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  • Laskov, P., Düssel, P., Schäfer, C., & Riech, K. (2005). Learning intrusion detection: supervised or unsupervised?. Paper presented at the International Conference on Image Analysis and Processing.

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  • Laskov, P., Rieck, K., Schäfer, C., & Müller, K.-R. (2005). Visualization of anomaly detection using prediction sensitivity. Paper presented at the In Sicherheit - Schutz und Zuverlässigkeit.

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  • Laskov, P., Schäfer, C., & Kotenko, I. (2004). Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines. Paper presented at the Detection of Intrusions and Malware & Vulnerability Assessment.

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  • von Wrede, C., & Laskov, P. (2004). Using classication to determine the number of n- ger strokes on a multi-touch tactile device. Paper presented at the European Symposium on Articial Neural Networks.

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  • Tasx, D., & Laskov, P. (2003). OnlineSVM learning: from classication to data description and back. Paper presented at the IEEE Workshop on Neural Networks for Signal Processing.

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  • Ziehe, A., Laskov, P., Müller, K.-R., & Nolte, G. (2003). A linear least-squares algorithm for joint diagonalization. Paper presented at the International Symposium on Independent Component Analysis and Blind Source Separation.

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  • Laskov, P., & Kambhamettu, C. (2002). Curvature-based algorithms for non-rigid motion and correspondence estimation. Paper presented at the Asian Conference on Computer Vision.

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  • Laskov, P., & Kambhamettu, C. (2001). Comparison of 3D algorithms for non-rigid motion and correspondence estimation. Paper presented at the British Machine Vision Conference.

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  • Laskov, P., & Kambhamettu, C. (2001). Kernel method for linear operator variational problems: non-linear unit normal algorithm for non-rigid motion estimation of 3D sur- faces. Paper presented at the Articial Neural NetworksICANN2001, workshop on kernel and subspace methods for computer vision.

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  • Laskov, P. (2000). An improved decomposition algorithm for regression support vector machines. , Advances in Neural Information Processing Systems 12.

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  • Laskov, P., & Kambhamettu, C. (2000). Tracking non-rigid objects using functional distance metric. Paper presented at the Indian Conference on Computer Vision, Graphics and Image Processing.

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  • Erenshteyn, R., Laskov, P., Saxe, D., & Foulds, R. (1999). Distributed output encoding for multi-class pattern recognition. Paper presented at the International Conference on Image Analysis and Processing.

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  • Erenshteyn, R., Laskov, P., & Foulds, R. (1997). Human gesture recognition using neural networks and multi-class encoding. Paper presented at the Articial Neural Networks in Engineering Conference.

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  • Erenshteyn, R., & Laskov, P. (1996). multi-stage approach to ngerspelling and gesture recognition. Paper presented at the Workshop on the Integration of Gesture in Language and Speech.

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  • Erenshteyn, R., Laskov, P., Foulds, R., Messing, L., & Stern, G. (1996). Recognition approach to gesture language understanding. Paper presented at the International Conference on Pattern Recognition.

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  • Krepki, R., Laskov, P., Curio, G., Blankertz, B., & Müller, K.-R. (2004). Berlin brain-computer interface. Nauka i Zhizn' (in Russian)(11).

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  • Laskov, P., & Vapnik, V. (1998). Model selection by structural risk minimization with l1 and l2 risk functionals. Paper presented at the Abstract at the European Conference on Computer Vision (ECCV), workshop on learning in computer vision.

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  • Laskov, P. (2001). Extensions of differential-geometric algorithms for estimation of 3D non-rigid motion and correspondence. Unpublished Doctoral Thesis, University of Delaware.

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