Pavel Laskov mainly investigates Artificial intelligence, Machine learning, Support vector machine, Classifier and Training set. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Data mining and Malware. His Malware study combines topics in areas such as Software and Discriminative model.
His work on Interpretability and Kernel as part of general Machine learning research is frequently linked to Linear combination and Lp space, bridging the gap between disciplines. His work deals with themes such as Adversary and Intrusion detection system, which intersect with Support vector machine. His Intrusion detection system study incorporates themes from Semi-supervised learning and Pattern recognition.
His primary areas of investigation include Artificial intelligence, Machine learning, Intrusion detection system, Anomaly detection and Computer security. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition. His studies in Machine learning integrate themes in fields like Adversarial system and Adversary.
His Anomaly detection research incorporates elements of Security analysis, Supervised learning and False positive paradox. A large part of his Computer security studies is devoted to Malware. He usually deals with Support vector machine and limits it to topics linked to Training set and Gradient descent and Data manipulation language.
His primary scientific interests are in Artificial intelligence, Machine learning, Classifier, Support vector machine and Malware. He interconnects Consistency and Optimization problem in the investigation of issues within Artificial intelligence. His studies deal with areas such as Adversarial system, Computer security, Adversary and Cryptovirology as well as Machine learning.
His research investigates the connection between Adversary and topics such as Statistical classification that intersect with problems in Adversarial machine learning and Artificial neural network. His Support vector machine research integrates issues from Training set and Kernel. His Malware research incorporates themes from Exploit, Information security, Effective method and Data mining.
Pavel Laskov spends much of his time researching Machine learning, Artificial intelligence, Support vector machine, Classifier and Training set. His study deals with a combination of Machine learning and Pre-play attack. Pavel Laskov integrates several fields in his works, including Pre-play attack, Statistical classification, Computer security, Artificial neural network and Adversarial machine learning.
His Artificial intelligence research is multidisciplinary, relying on both Adversary and Malware. The Learning based study combines topics in areas such as Feature extraction and Data mining. Many of his studies on Intrusion detection system involve topics that are commonly interrelated, such as Data manipulation language.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Support Vector Machines
Konrad Rieck;Sören Sonnenburg;Sebastian Mika;Christin Schäfer.
Learning and Classification of Malware Behavior
Konrad Rieck;Thorsten Holz;Carsten Willems;Patrick Düssel.
international conference on detection of intrusions and malware and vulnerability assessment (2008)
Evasion attacks against machine learning at test time
Battista Biggio;Igino Corona;Davide Maiorca;Blaine Nelson.
european conference on machine learning (2013)
Incremental Support Vector Learning: Analysis, Implementation and Applications
Pavel Laskov;Christian Gehl;Stefan Krüger;Klaus-Robert Müller;Klaus-Robert Müller.
Journal of Machine Learning Research (2006)
Poisoning Attacks against Support Vector Machines
Battista Biggio;Blaine Nelson;Pavel Laskov.
arXiv: Learning (2012)
A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation
Andreas Ziehe;Pavel Laskov;Guido Nolte;Klaus-Robert Müller;Klaus-Robert Müller.
Journal of Machine Learning Research (2004)
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Marius Kloft;Ulf Brefeld;Pavel Laskov;Klaus-Robert Müller.
neural information processing systems (2009)
Practical Evasion of a Learning-Based Classifier: A Case Study
Nedim rndic;Pavel Laskov.
ieee symposium on security and privacy (2014)
Learning intrusion detection: supervised or unsupervised?
Pavel Laskov;Patrick Düssel;Christin Schäfer;Konrad Rieck.
international conference on image analysis and processing (2005)
Support Vector Machines Under Adversarial Label Noise
Battista Biggio;Blaine Nelson;Pavel Laskov.
asian conference on machine learning (2011)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: