The scientist’s investigation covers issues in Computer security, Artificial intelligence, Malware, Machine learning and Static analysis. Konrad Rieck works mostly in the field of Computer security, limiting it down to concerns involving The Internet and, occasionally, Computer virus, Computer network, High availability and Host. His work in Artificial intelligence tackles topics such as Pattern recognition which are related to areas like Pairwise comparison, String and Data structure.
His study in Malware is interdisciplinary in nature, drawing from both Android and Evasion. His study looks at the intersection of Machine learning and topics like Anomaly detection with Intrusion detection system, Language model, Process and Key. His Static analysis study also includes fields such as
His main research concerns Computer security, Artificial intelligence, Malware, Machine learning and Intrusion detection system. His research on Computer security also deals with topics like
Konrad Rieck studies Malware, focusing on Cryptovirology in particular. His Machine learning study also includes
Konrad Rieck spends much of his time researching Computer security, Artificial neural network, Artificial intelligence, Malware and Image scaling. His research is interdisciplinary, bridging the disciplines of Variety and Computer security. Konrad Rieck frequently studies issues relating to Behavioral pattern and Artificial neural network.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Vulnerability discovery. Many of his research projects under Machine learning are closely connected to Work and TRACE with Work and TRACE, tying the diverse disciplines of science together. The Vulnerability discovery study combines topics in areas such as Deep learning and Robustness.
Konrad Rieck focuses on Image scaling, Artificial neural network, Artificial intelligence, Backdoor and Computer security. His study of Image scaling brings together topics like Preprocessor, Machine learning and Adversarial system. Konrad Rieck combines subjects such as Vulnerability discovery, Deep learning, Malware, Robustness and Data science with his study of Artificial neural network.
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.
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.
Daniel Arp;Michael Spreitzenbarth;Malte Hubner;Hugo Gascon.
network and distributed system security symposium (2014)
Automatic analysis of malware behavior using machine learning
Konrad Rieck;Philipp Trinius;Carsten Willems;Thorsten Holz_aff n.
Journal of Computer Security (2011)
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)
Measuring and Detecting Fast-Flux Service Networks
Thorsten Holz;Christian Gorecki;Konrad Rieck;Felix C. Freiling.
network and distributed system security symposium (2008)
Structural detection of android malware using embedded call graphs
Hugo Gascon;Fabian Yamaguchi;Daniel Arp;Konrad Rieck.
Proceedings of the 2013 ACM workshop on Artificial intelligence and security (2013)
Modeling and Discovering Vulnerabilities with Code Property Graphs
Fabian Yamaguchi;Nico Golde;Daniel Arp;Konrad Rieck.
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)
Toward supervised anomaly detection
Nico Görnitz;Marius Kloft;Konrad Rieck;Ulf Brefeld.
Journal of Artificial Intelligence Research (2013)
Cujo: efficient detection and prevention of drive-by-download attacks
Konrad Rieck;Tammo Krueger;Andreas Dewald.
annual computer security applications conference (2010)
Profile was last updated on December 6th, 2021.
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