uni.liPersonenverzeichnis

Prof. Dr. Pavel Laskov

Professor
Data & Application Security
Studienleiter MSc IS
Liechtenstein Business School
Tätigkeit
Pavel Laskov ist Professor für Daten- und Anwendungssicherheit. Seine Forschung befasst sich mit der Entwicklung von Techniken zur Erkennung und Aufklärung von Sicherheitsvorfällen. Dabei werden als wichtiges technisches Werkzeug die auf Cybersicherheit abgestimmte KI-Techniken eingesetzt. Neben den klassischen Zielen der Angriffserkennng eignen sich KI-Techniken insbesondere für fortgeschrittene Aufgaben wie Analyse von Logdateien, Korrelation der Alarme, forensische Untersuchungen sowie Threat-Intelligence. Auf Grund der steigenden Bedeutung der KI, insbesondere in sicherheitskritischen Systemen wie z.B. 5G-Netzwerke, steht die Erforschung der Angriffe auf KI-Systeme sowie der geeigneten Gegenmassnahmen auch auf der Forschungsagenda des Lehrstuhls.
Ausbildung
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, Diplom in Computer and Systems Engineering

Werdegang
seit 2018

Universität Liechtenstein, Professor. Inhaber des Hilti Lehrstuhls für Daten- und Anwendungssicherheit

2014 — 2018

Huawei European Research Center (München), Principal Engineer, Leiter des "Big Data Threat Analysis" Teams

2009 — 2014

Universität Tübingen, Heisenberg Stipendiat

2001 — 2009

Fraunhofer Institut für Rechnerarchitektur und Softwaretechnik (Berlin), Projekt- und Forschungsgruppenleiter

1996 — 2001

University of Delaware (USA), Doktorand

1996

AT&T Research (USA), Praktikant

Portrait
Veranstaltungen im WS 23/24
Sicherheit der künstlichen Intelligenz im Finanzwesen
Vorstudie zur Dissertation, seit Februar 2024

Dieses Forschungsprojekt untersucht die Risiken und Schwachstellen beim Einsatz von maschinellem Lernen in verschiedenen Finanzfunktionen. Ziel ist es, Methoden zu entwickeln, um diese Risiken zu ... mehr

Domänenintegrierte Produktempfehlungen für ein B2B-Unternehmen: Ein einheitliches Recommender-System
Dissertation, seit August 2023

Erhebliche Veränderungen im Kaufverhalten der Verbraucher haben die Entwicklung von Entwicklung von Empfehlungssystemen revolutioniert. B2C-Unternehmen waren führend bei der die fortschrittlichsten ... mehr

Ein hybrider Rahmen für die Produktentwicklung unter Verwendung von Simulation, überwachtem Lernen und mathematischer Optimierung
Dissertation, seit August 2023

folgt mehr

Sensorbasierte Aktivitätserkennung für handgeführte Geräte auf der Baustelle
Vorstudie zur Dissertation, seit Februar 2022

In der Baubranche ist mangelnde Transparenz in Bezug auf Produktivität, Arbeitsfortschritt und Gerätenutzung ein bekanntes Problem. Die fortschreitende Digitalisierung gilt in diesem Kontext als ... mehr

A Comprehensive Examination of Offensive AI's Capabilities and Limitations in Cyberattacks
Dissertation, seit September 2021

The cyber threat landscape is constantly evolving, with new tools and technological advancements changing how adversaries conduct cyberattacks. With the progression of Artificial Intelligence (AI) ... mehr

Sicherheit künstlich intelligenter Systeme in 5G-Netzwerken
Dissertation, seit Februar 2021

Die Einführung der 5G-Technologie in der Telekommunikation führt, in Verbindung mit dem schnellen Wachstum der Anzahl, Verschiedenartigkeit und Dichte von verbundenen Geräten, zu zahlreichen ... mehr

Alliance for developing, teaching and training Digital Forensics and Incident Response students and practitioners
ERASMUS, September 2020 bis August 2023

Das Projekt DFIR-Allianz (Digital Forensics Incident Response) formt eine Expertengruppe auf diesem Gebiet wird einen aktuellen Lehrplan für diesen Bereich entwickelen. Zunächst wird die Allianz ... mehr

Meeting Industrial Demand for Skills in Information Security Education
ERASMUS, Oktober 2019 bis September 2021

The project will identify skill gaps in the information security education and develop recommendations for curricula design in order to minimize such gaps. The project's methodology is based on a ... mehr

Detection of Malicious Cryptomining in Network Metadata
FFF-Förderprojekt, Dezember 2018 bis Januar 2019 (abgeschlossen)

Kryptowährungen und denen zu Grunde liegenden Blockchain-Technologien sind eine der faszinierendsten Entwicklungen in der Informationstechnologie im letzten Jahrzehnt. Der Markt für Kryptowährungen ... mehr

  • Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Burdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2022). The Role of Machine Learning in Cybersecurity. ACM Digital Threats: Research and Practice.

    details
  • Apruzzese, G., Vladimirov, R., Tastemirova, A., & Laskov, P. (2022). Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. IEEE Transactions on Network and Service Management (TNSM).

    details
  • Russo, M., Srndic, N., & Laskov, P. (2021). Detection of illicit cryptomining using network metadata. EURASIP Journal on Information Security(11).

    details
  • Srndic, N., & Laskov, P. (2016). Hidost: a static machine-learning-based detector of malicious files. EURASIP Journal on Information Security, 2016(22).

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

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

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

    details
  • 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.

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

    details
  • Krüger, T., Gehl, C., Rieck, K., & Laskov, P. (2008). An architecture for inline anomaly detection. European Conference on Computer Network Defense, 11-18.

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

    details
  • 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.

    details
  • 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.

    details
  • 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.

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

    details
  • 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.

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

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

    details
  • 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.

    details
  • 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.

    details
  • Apruzzese, G., Laskov, P., & Schneider, J. (2023). SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. Paper presented at the IEEE European Symposium on Security and Privacy (IEEE EuroS&P), Delft, Netherlands.

    details
  • Apruzzese, G., Tastemirova, A., & Laskov, P. (2022). SoK: The Impact of Unlabelled Data for Cyberthreat Detection. Paper presented at the IEEE European Symposium on Security and Privacy (EuroSP).

    details
  • Goupil, F., Laskov, P., Pearic, I., Felderer, M., Duerr, A., & Thiesse, F. (2022). Understanding the Skil Gap in Cybersecurity. Paper presented at the Proceedings of 27th Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE).

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

    details
  • 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.

    details
  • 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.

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

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

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

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

    details
  • 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.

    details
  • 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.

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

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

    details
  • 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.

    details
  • 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.

    details
  • 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.

    details
  • 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.

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

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

    details
  • 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.

    details
  • 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.

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

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

    details
  • 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.

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

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

    details
  • 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.

    details
  • 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.

    details
  • 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.

    details
  • 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.

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

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

    details
  • 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.

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

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

    details
  • 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 Artificial Neural Networks - ICANN2001, workshop on kernel and subspace methods for computer vision.

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

    details
  • 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.

    details
  • 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.

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

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

    details
  • 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.

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

    details