- Professor
- Data & Application Security
- Academic Director MSc IS
- Liechtenstein Business School
- Current Activity
- Pavel Laskov is Professor for Data and Application Security. His research is focused on the development of techniques for detection and mitigation of security incidents. The main technical instrument of this research are specialized AI techniques for cybersecurity. Besides the classical problems of intrusion detection, AI techniques are essential for log data analysis, alert correlation, forensic investigation as well as threat intelligence. Due to the growing importance of AI, especially in high-risk applications and critical infrastructures such as 5G networks, an important topic of the Chair's research agenda is investigation of attacks against AI systems and appropriate countermeasures.
- 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 — 2014
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
- Schedule for WS 23/24
- _Introduction MSc Information Systems (Introduction) Laskov
- Bachelor's thesis: Process (Thesis)
- C19 Autonomous Tools, Design, and Innovation (Module)
- C19 Business Process Analysis (Module)
- C19 Business Statistics (Module)
- C19 Data Management (CPE) (Module)
- C19 Data Visualisation (Module)
- C19 Digital Entrepreneurship (MSc IS 19) (Module)
- C19 Emerging IT Topics (Module/Course/Examination)
- C19 Information Systems Modelling (Module)
- C19 Intrusion Detection and Mitigation (Module)
- C19 Master's thesis (Module)
- C19 Network and System Security (Module)
- C19 Process Mining (Module)
- C19 Research Seminar (Module/Course/Examination)
- C21_Systems Analysis and Design (Module)
- Emerging IT Topics (Seminar) Laskov, Pekaric, Schneider, Hacker, Gau
- Extracurriculare activities (Module)
- Intrusion Detection and Mitigation (Lecture) Laskov, Pekaric, Apruzzese
- Master's thesis (Thesis) vom Brocke, Apruzzese, Laskov, Schneider, Hacker
- Network and System Security - Exercise (Exercise) Laskov, Pekaric
- Network and System Security - Lecture (Lecture) Laskov, Pekaric
- Presentation and Defence (Miscellaneous) Laskov, Schenk
- Project Seminar (Seminar) Schneider, Laskov
- Research Colloquium (Seminar) Laskov
- Research Colloquium (Module/Course/Examination)
- Research Design and Management (Lecture / Exercise) Bayrle-Kelso, Brecht, Chandra Kruse, Furtner, Hanke, Kirn, Laskov, Schenk, Schneider, Stöckl
- Research Methods (Lecture) Laskov, Schneider, Hacker, Apruzzese, Schenk, Chandra Kruse
- Research Seminar (Seminar) Laskov, Hacker, Apruzzese, Schneider, Schenk, Gau
- Security of Artificial Intelligence in Finance
- Preproposal PhD-Thesis, since February 2024
This research project explores the genuine risks and vulnerabilities associated with leveraging machine learning across various financial functions. The aim is to develop methodologies for ... more ...
- Domain integrated product recommendations for a B2B enterprise: A Unified Recommender System
- PhD-Thesis, since August 2023
Significant changes in the consumer's purchasing behavior have revolutionized the development of recommender systems. B2C enterprises have been at the forefront in producing the most advanced ... more ...
- A hybrid framework for product development using simulation, supervised learning and mathematical optimization
- PhD-Thesis, since August 2023
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- Sensor-based activity recognition for hand-held power tools at the construction site
- Preproposal PhD-Thesis, since February 2022
Nowadays, the construction sector still lacks transparency in terms of productivity, work progress and tool utilization on the job site. The ongoing digitalization of the construction industry is ... more ...
- A Comprehensive Examination of Offensive AI's Capabilities and Limitations in Cyberattacks
- PhD-Thesis, since 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) ... more ...
- Security of Artificial Intelligence Systems in 5G Networks
- PhD-Thesis, since February 2021
The adoption of the 5G technology in telecommunications and rapid growth in numbers, variety and density of connected devices raises serious security concerns. The increasing amount of network ... more ...
- Alliance for developing, teaching and training Digital Forensics and Incident Response students and practitioners
- ERASMUS, September 2020 until August 2023
This project aims to form a Digital Forensics Incident Response (DFIR) Alliance who can serve as experts in the domain but more importantly will develop an up to date curricula for this domain. ... more ...
- Meeting Industrial Demand for Skills in Information Security Education
- ERASMUS, October 2019 until 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 ... more ...
- Detection of Malicious Cryptomining in Network Metadata
- FFF-Förderprojekt, December 2018 until January 2019 (finished)
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 ...
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.
moreApruzzese, 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).
moreRusso, M., Srndic, N., & Laskov, P. (2021). Detection of illicit cryptomining using network metadata. EURASIP Journal on Information Security(11).
moreSrndic, N., & Laskov, P. (2016). Hidost: a static machine-learning-based detector of malicious files. EURASIP Journal on Information Security, 2016(22).
moreGrozea, C., & Laskov, P. (2012). Anomaly detection at "supersonic" speed. Information Technology, 54(2), 82-89.
moreKloft, M., & Laskov, P. (2012). Security analysis of online centroid anomaly detection. Journal of Machine Learning Research, 13(1), 3133-3176.
moreHenneges, 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.
moreWahl, 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.
moreRieck, K., & Laskov, P. (2008). Linear-time computation of similarity measures for sequential data. Journal of Machine Learning Research, 9, 23-48.
moreKrüger, T., Gehl, C., Rieck, K., & Laskov, P. (2008). An architecture for inline anomaly detection. European Conference on Computer Network Defense, 11-18.
moreRieck, K., & Laskov, P. (2007). Language models for detection of unknown attacks in network traffic. Journal in Computer Virology, 2(4), 243-256.
moreLaskov, 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.
moreLaskov, 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.
moreZiehe, 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.
moreKambhamettu, C., Goldgof, D., He, M., & Laskov, P. (2003). 3D nonrigid motion analysis under small deformations. Image and Vision Computing, 21(3), 229-245.
moreLaskov, 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.
moreLaskov, 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.
moreMika, 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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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.
moreApruzzese, 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).
moreGoupil, 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).
moreSrndic, N., & Laskov, P. (2014). Practical evasion of a learning-based classifier: A case study. Paper presented at the IEEE Symposium on Security and Privacy.
moreBiggio, 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.
moreSrndic, N., & Laskov, P. (2013). Detection of malicious PDF files based on hierarchical document structure. Paper presented at the 20th Network and Distributed Systems Symposium.
moreBiggio, B., Nelson, B., & Laskov, P. (2012). Poisoning attacks against support vector machines. Paper presented at the International Conference on Machine Learning.
moreBiggio, B., Nelson, B., & Laskov, P. (2011). Support vector machines under adversarial label noise. Paper presented at the Asian Conference on Machine Learning.
moreLaskov, P., & Srndic, N. (2011). Static detection of malicious JavaScript-bearing PDF documents. Paper presented at the Annual Computer Security Applications Conference.
moreNelson, B., Biggio, B., & Laskov, P. (2011). Microbagging estimators: An ensemble approach to distance-weighted classifiers. Paper presented at the Asian Conference on Machine Learning.
moreNelson, 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.
moreGrozea, 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.
moreKloft, 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.
moreKlkoft, M., & Laskov, P. (2010). Online anomaly detection under adversarial impact. Paper presented at the Proceedings of the International Conference on Artificial Intelligence and Statistics.
moreKrüger, T., Gehl, C., Rieck, K., & Laskov, P. (2010). TokDoc: A self-healing web application firewall. Paper presented at the ACM Symposium on Applied Computing.
moreRieck, 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.
moreDüssel, 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.
moreHaage, 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.
moreLaskov, P., & Kloft, M. (2009). A framework for quantitative security analysis of machine learning. Paper presented at the ACM Workshop on Artificial Intelligence and Security.
moreRieck, K., & Laskov, P. (2009). Visualization and explanation of payload-based anomaly detection. Paper presented at the European Conference on Computer Network Defense.
moreDüssel, 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.
moreFranc, 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.
moreKloft, 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.
moreRieck, 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.
moreRieck, 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.
moreRieck, 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.
moreRieck, K., & Laskov, P. (2006). Detecting unknown network attacks using language models. Paper presented at the Detection of Intrusions and Malware & Vulnerability Assessment.
moreRieck, 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.
moreLaskov, 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.
moreLaskov, 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.
moreLaskov, 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.
morevon 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.
moreTasx, 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.
moreZiehe, 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.
moreLaskov, P., & Kambhamettu, C. (2002). Curvature-based algorithms for non-rigid motion and correspondence estimation. Paper presented at the Asian Conference on Computer Vision.
moreLaskov, P., & Kambhamettu, C. (2001). Comparison of 3D algorithms for non-rigid motion and correspondence estimation. Paper presented at the British Machine Vision Conference.
moreLaskov, 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.
moreLaskov, P. (2000). An improved decomposition algorithm for regression support vector machines. , Advances in Neural Information Processing Systems 12.
moreLaskov, 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.
moreErenshteyn, 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.
moreErenshteyn, 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.
moreErenshteyn, 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.
moreErenshteyn, 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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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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