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Konrad Rieck
Person information
- affiliation: TU Berlin, Germany
- affiliation (former): TU Braunschweig, Institute of System Security, Germany
- affiliation (former): University of Göttingen, Institute of Computer Science, Germany
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2020 – today
- 2024
- [j20]Micha Horlboge, Erwin Quiring, Roland Meyer, Konrad Rieck:
I still know it's you! On Challenges in Anonymizing Source Code. Proc. Priv. Enhancing Technol. 2024(3): 744-760 (2024) - [c91]Alwin Maier
, Felix Weißberg
On the Role of Pre-trained Embeddings in Binary Code Analysis. AsiaCCS 2024 - [c90]Jonas Möller
Cross-Language Differential Testing of JSON Parsers. AsiaCCS 2024 - [c89]Felix Weissberg
SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing. AsiaCCS 2024 - [c88]Moritz Pfister, Robert Michael, Max Boll, Cosima Körfer, Konrad Rieck, Daniel Arp:
Listening Between the Bits: Privacy Leaks in Audio Fingerprints. DIMVA 2024: 184-204 - [c87]Robin Kirchner, Jonas Möller, Marius Musch, David Klein, Konrad Rieck, Martin Johns:
Dancer in the Dark: Synthesizing and Evaluating Polyglots for Blind Cross-Site Scripting. USENIX Security Symposium 2024 - [i29]Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov:
Manipulating Feature Visualizations with Gradient Slingshots. CoRR abs/2401.06122 (2024) - 2023
- [j19]Daniel Arp
Lessons Learned on Machine Learning for Computer Security. IEEE Secur. Priv. 21(5): 72-77 (2023) - [c86]Erwin Quiring
On the Detection of Image-Scaling Attacks in Machine Learning. ACSAC 2023: 506-520 - [c85]Tom Ganz
PAVUDI: Patch-based Vulnerability Discovery using Machine Learning. ACSAC 2023: 704-717 - [c84]Konrad Rieck
When Papers Choose Their Reviewers: Adversarial Machine Learning in Peer Review. ARTMAN@CCS 2023: 3 - [c83]Erik Imgrund
Broken Promises: Measuring Confounding Effects in Learning-based Vulnerability Discovery. AISec@CCS 2023: 149-160 - [c82]Tom Ganz
Detecting Backdoors in Collaboration Graphs of Software Repositories. CODASPY 2023: 189-200 - [c81]Tom Ganz, Erik Imgrund, Martin Härterich, Konrad Rieck:
CodeGraphSMOTE - Data Augmentation for Vulnerability Discovery. DBSec 2023: 282-301 - [c80]Lukas Seidel
Learning Type Inference for Enhanced Dataflow Analysis. ESORICS (4) 2023: 184-203 - [c79]Tom Ganz, Philipp Rall, Martin Härterich, Konrad Rieck:
Hunting for Truth: Analyzing Explanation Methods in Learning-based Vulnerability Discovery. EuroS&P 2023: 524-541 - [c78]Stefan Czybik
Lazy Gatekeepers: A Large-Scale Study on SPF Configuration in the Wild. IMC 2023: 344-355 - [c77]Alexander Warnecke, Lukas Pirch
Machine Unlearning of Features and Labels. NDSS 2023 - [c76]Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel, Thorsten Holz, Konrad Rieck:
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning. USENIX Security Symposium 2023: 5109-5126 - [i28]Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel, Thorsten Holz, Konrad Rieck:
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning. CoRR abs/2303.14443 (2023) - [i27]Alexander Warnecke, Julian Speith
Evil from Within: Machine Learning Backdoors through Hardware Trojans. CoRR abs/2304.08411 (2023) - [i26]Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe, Fabian Yamaguchi:
Learning Type Inference for Enhanced Dataflow Analysis. CoRR abs/2310.00673 (2023) - [i25]Erwin Quiring, Andreas Müller, Konrad Rieck:
On the Detection of Image-Scaling Attacks in Machine Learning. CoRR abs/2310.15085 (2023) - 2022
- [c75]Stefan Czybik, Daniel Arp, Konrad Rieck:
Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing. CODASPY 2022: 264-275 - [c74]Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring:
Misleading Deep-Fake Detection with GAN Fingerprints. SP (Workshops) 2022: 59-65 - [c73]Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck:
Dos and Don'ts of Machine Learning in Computer Security. USENIX Security Symposium 2022: 3971-3988 - [i24]Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring:
Misleading Deep-Fake Detection with GAN Fingerprints. CoRR abs/2205.12543 (2022) - [i23]Micha Horlboge
I still know it's you! On Challenges in Anonymizing Source Code. CoRR abs/2208.12553 (2022) - [i22]Battista Biggio, Nicholas Carlini, Pavel Laskov, Konrad Rieck, Antonio Emanuele Cinà:
Security of Machine Learning (Dagstuhl Seminar 22281). Dagstuhl Reports 12(7): 41-61 (2022) - 2021
- [j18]Alexandra Dirksen, David Klein
LogPicker: Strengthening Certificate Transparency Against Covert Adversaries. Proc. Priv. Enhancing Technol. 2021(4): 184-202 (2021) - [c72]Niclas Kühnapfel, Stefan Preußler, Maximilian Noppel
LaserShark: Establishing Fast, Bidirectional Communication into Air-Gapped Systems. ACSAC 2021: 796-811 - [c71]Jan Malte Hilgefort
Spying through Virtual Backgrounds of Video Calls. AISec@CCS 2021: 135-144 - [c70]Tom Ganz, Martin Härterich, Alexander Warnecke, Konrad Rieck:
Explaining Graph Neural Networks for Vulnerability Discovery. AISec@CCS 2021: 145-156 - [c69]Lukas Pirch
TagVet: Vetting Malware Tags using Explainable Machine Learning. EuroSec@EuroSys 2021: 34-40 - [i21]Niclas Kühnapfel, Stefan Preußler, Maximilian Noppel, Thomas Schneider, Konrad Rieck, Christian Wressnegger:
LaserShark: Establishing Fast, Bidirectional Communication into Air-Gapped Systems. CoRR abs/2106.04119 (2021) - [i20]Alexander Warnecke, Lukas Pirch, Christian Wressnegger, Konrad Rieck:
Machine Unlearning of Features and Labels. CoRR abs/2108.11577 (2021) - [i19]André Noll Barreto, Stefan Köpsell, Arsenia Chorti, Bertram Poettering, Jens Jelitto, Julia Hesse, Jonathan Boole, Konrad Rieck, Marios Kountouris, Dave Singelée, Kumar Ashwinee:
Towards Intelligent Context-Aware 6G Security. CoRR abs/2112.09411 (2021) - 2020
- [c68]Ansgar Kellner, Christian Wressnegger, Konrad Rieck:
What's all that noise: analysis and detection of propaganda on Twitter. EuroSec@EuroSys 2020: 25-30 - [c67]Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck:
Evaluating Explanation Methods for Deep Learning in Security. EuroS&P 2020: 158-174 - [c66]Michele Scalas
Explanation-Driven Characterization of Android Ransomware. ICPR Workshops (3) 2020: 228-242 - [c65]Erwin Quiring, Konrad Rieck:
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks. SP (Workshops) 2020: 41-47 - [c64]Erwin Quiring, David Klein, Daniel Arp, Martin Johns, Konrad Rieck:
Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning. USENIX Security Symposium 2020: 1363-1380 - [i18]Erwin Quiring, Konrad Rieck:
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks. CoRR abs/2003.08633 (2020) - [i17]Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck:
Dos and Don'ts of Machine Learning in Computer Security. CoRR abs/2010.09470 (2020) - [i16]Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck:
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification. CoRR abs/2010.09569 (2020)
2010 – 2019
- 2019
- [j17]Ambra Demontis
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. IEEE Trans. Dependable Secur. Comput. 16(4): 711-724 (2019) - [c63]Marius Musch, Christian Wressnegger, Martin Johns
Thieves in the Browser: Web-based Cryptojacking in the Wild. ARES 2019: 4:1-4:10 - [c62]Marius Musch, Christian Wressnegger, Martin Johns
New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild. DIMVA 2019: 23-42 - [c61]Alwin Maier, Hugo Gascon, Christian Wressnegger, Konrad Rieck:
TypeMiner: Recovering Types in Binary Programs Using Machine Learning. DIMVA 2019: 288-308 - [c60]Erwin Quiring, Matthias Kirchner, Konrad Rieck:
On the Security and Applicability of Fragile Camera Fingerprints. ESORICS (1) 2019: 450-470 - [c59]Ansgar Kellner, Micha Horlboge
False Sense of Security: A Study on the Effectivity of Jailbreak Detection in Banking Apps. EuroS&P 2019: 1-14 - [c58]Erwin Quiring, Alwin Maier, Konrad Rieck:
Misleading Authorship Attribution of Source Code using Adversarial Learning. USENIX Security Symposium 2019: 479-496 - [i15]Erwin Quiring, Alwin Maier, Konrad Rieck:
Misleading Authorship Attribution of Source Code using Adversarial Learning. CoRR abs/1905.12386 (2019) - [i14]Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck:
Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security. CoRR abs/1906.02108 (2019) - [i13]Erwin Quiring, Matthias Kirchner, Konrad Rieck:
On the Security and Applicability of Fragile Camera Fingerprints. CoRR abs/1907.04025 (2019) - [i12]Ansgar Kellner, Lisa Rangosch, Christian Wressnegger, Konrad Rieck:
Political Elections Under (Social) Fire? Analysis and Detection of Propaganda on Twitter. CoRR abs/1912.04143 (2019) - 2018
- [c57]Konrad Rieck:
Family Reunion: Adversarial Machine Learning meets Digital Watermarking. MPS@CCS 2018: 1 - [c56]Christian Wressnegger, Ansgar Kellner, Konrad Rieck:
ZOE: Content-Based Anomaly Detection for Industrial Control Systems. DSN 2018: 127-138 - [c55]Erwin Quiring, Daniel Arp, Konrad Rieck:
Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking. EuroS&P 2018: 488-502 - [c54]Erwin Quiring, Konrad Rieck:
Adversarial Machine Learning Against Digital Watermarking. EUSIPCO 2018: 519-523 - [c53]Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard E. Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan:
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries. NDSS 2018 - [c52]Hugo Gascon, Steffen Ullrich, Benjamin Stritter, Konrad Rieck:
Reading Between the Lines: Content-Agnostic Detection of Spear-Phishing Emails. RAID 2018: 69-91 - [c51]Daniel Arp, Erwin Quiring, Tammo Krueger, Stanimir Dragiev, Konrad Rieck:
Privacy-Enhanced Fraud Detection with Bloom Filters. SecureComm (1) 2018: 396-415 - [e2]Angelos Stavrou, Konrad Rieck:
Proceedings of the 11th European Workshop on Systems Security, EuroSec@EuroSys 2018, Porto, Portugal, April 23, 2018. ACM 2018 [contents] - [i11]Marius Musch, Christian Wressnegger, Martin Johns, Konrad Rieck:
Web-based Cryptojacking in the Wild. CoRR abs/1808.09474 (2018) - [i10]Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, Fabio Roli:
Poisoning Behavioral Malware Clustering. CoRR abs/1811.09985 (2018) - 2017
- [j16]Konrad Rieck:
Vulnerability analysis. it Inf. Technol. 59(2): 57 (2017) - [j15]Christian Wressnegger, Fabian Yamaguchi, Alwin Maier, Konrad Rieck:
64-Bit Migration Vulnerabilities. it Inf. Technol. 59(2): 73 (2017) - [c50]Christian Wressnegger, Kevin Freeman, Fabian Yamaguchi, Konrad Rieck:
Automatically Inferring Malware Signatures for Anti-Virus Assisted Attacks. AsiaCCS 2017: 587-598 - [c49]Hugo Gascon, Bernd Grobauer
Mining Attributed Graphs for Threat Intelligence. CODASPY 2017: 15-22 - [c48]Christian Wressnegger, Konrad Rieck:
Looking Back on Three Years of Flash-based Malware. EUROSEC 2017: 6:1-6:6 - [c47]David Goltzsche, Colin Wulf, Divya Muthukumaran, Konrad Rieck, Peter R. Pietzuch, Rüdiger Kapitza:
TrustJS: Trusted Client-side Execution of JavaScript. EUROSEC 2017: 7:1-7:6 - [c46]Daniel Arp, Erwin Quiring, Christian Wressnegger, Konrad Rieck:
Privacy Threats through Ultrasonic Side Channels on Mobile Devices. EuroS&P 2017: 35-47 - [c45]Michael Backes, Konrad Rieck, Malte Skoruppa, Ben Stock
Efficient and Flexible Discovery of PHP Application Vulnerabilities. EuroS&P 2017: 334-349 - [c44]Bhargava Shastry, Markus Leutner, Tobias Fiebig
Static Program Analysis as a Fuzzing Aid. RAID 2017: 26-47 - [c43]Bhargava Shastry, Federico Maggi, Fabian Yamaguchi, Konrad Rieck, Jean-Pierre Seifert:
Static Exploration of Taint-Style Vulnerabilities Found by Fuzzing. WOOT 2017 - [c42]Tommi Unruh, Bhargava Shastry, Malte Skoruppa, Federico Maggi, Konrad Rieck, Jean-Pierre Seifert, Fabian Yamaguchi:
Leveraging Flawed Tutorials for Seeding Large-Scale Web Vulnerability Discovery. WOOT 2017 - [i9]Erwin Quiring, Daniel Arp, Konrad Rieck:
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking. CoRR abs/1703.05561 (2017) - [i8]Tommi Unruh, Bhargava Shastry, Malte Skoruppa, Federico Maggi, Konrad Rieck, Jean-Pierre Seifert, Fabian Yamaguchi:
Leveraging Flawed Tutorials for Seeding Large-Scale Web Vulnerability Discovery. CoRR abs/1704.02786 (2017) - [i7]Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli:
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. CoRR abs/1704.08996 (2017) - [i6]Bhargava Shastry, Federico Maggi, Fabian Yamaguchi, Konrad Rieck, Jean-Pierre Seifert:
Static Exploration of Taint-Style Vulnerabilities Found by Fuzzing. CoRR abs/1706.00206 (2017) - 2016
- [j14]Fabian Yamaguchi, Konrad Rieck:
Die Codeanalyseplattform "Octopus". Datenschutz und Datensicherheit 40(11): 713-717 (2016) - [j13]Konrad Rieck, Christian Wressnegger:
Harry: A Tool for Measuring String Similarity. J. Mach. Learn. Res. 17: 9:1-9:5 (2016) - [j12]Andreas Kurtz, Hugo Gascon, Tobias Becker, Konrad Rieck, Felix C. Freiling:
Fingerprinting Mobile Devices Using Personalized Configurations. Proc. Priv. Enhancing Technol. 2016(1): 4-19 (2016) - [c41]Christian Wressnegger, Fabian Yamaguchi, Alwin Maier, Konrad Rieck:
Twice the Bits, Twice the Trouble: Vulnerabilities Induced by Migrating to 64-Bit Platforms. CCS 2016: 541-552 - [c40]Bhargava Shastry, Fabian Yamaguchi, Konrad Rieck, Jean-Pierre Seifert:
Towards Vulnerability Discovery Using Staged Program Analysis. DIMVA 2016: 78-97 - [c39]Christian Wressnegger, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Comprehensive Analysis and Detection of Flash-Based Malware. DIMVA 2016: 101-121 - [c38]Salke Hartung, Ansgar Kellner, Konrad Rieck, Dieter Hogrefe:
Monte Carlo Localization for path-based mobility in mobile wireless sensor networks. WCNC 2016: 1-7 - [i5]Christian Wressnegger, Kevin Freeman, Fabian Yamaguchi, Konrad Rieck:
From Malware Signatures to Anti-Virus Assisted Attacks. CoRR abs/1610.06022 (2016) - 2015
- [c37]Henning Perl, Sergej Dechand, Matthew Smith, Daniel Arp, Fabian Yamaguchi, Konrad Rieck, Sascha Fahl, Yasemin Acar:
VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits. CCS 2015: 426-437 - [c36]Daniel Arp, Fabian Yamaguchi, Konrad Rieck:
Torben: A Practical Side-Channel Attack for Deanonymizing Tor Communication. AsiaCCS 2015: 597-602 - [c35]Hugo Gascon, Christian Wressnegger, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols. SecureComm 2015: 330-347 - [c34]Fabian Yamaguchi, Alwin Maier, Hugo Gascon, Konrad Rieck:
Automatic Inference of Search Patterns for Taint-Style Vulnerabilities. IEEE Symposium on Security and Privacy 2015: 797-812 - [i4]Aylin Caliskan Islam, Fabian Yamaguchi, Edwin Dauber, Richard E. Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan:
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries. CoRR abs/1512.08546 (2015) - 2014
- [j11]Shujun Li
Special issue on threat detection, analysis and defense. J. Inf. Secur. Appl. 19(3): 163-164 (2014) - [c33]Battista Biggio
Poisoning behavioral malware clustering. AISec@CCS 2014: 27-36 - [c32]Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck:
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. NDSS 2014 - [c31]Hugo Gascon, Sebastian Uellenbeck, Christopher Wolf, Konrad Rieck:
Continuous Authentication on Mobile Devices by Analysis of Typing Motion Behavior. Sicherheit 2014: 1-12 - [c30]Fabian Yamaguchi, Nico Golde, Daniel Arp, Konrad Rieck:
Modeling and Discovering Vulnerabilities with Code Property Graphs. IEEE Symposium on Security and Privacy 2014: 590-604 - [i3]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. CoRR abs/1401.6424 (2014) - 2013
- [j10]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. J. Artif. Intell. Res. 46: 235-262 (2013) - [c29]Konrad Rieck:
Off the beaten path: machine learning for offensive security. AISec 2013: 1-2 - [c28]Hugo Gascon, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Structural detection of android malware using embedded call graphs. AISec 2013: 45-54 - [c27]Christian Wressnegger, Guido Schwenk, Daniel Arp, Konrad Rieck:
A close look on n-grams in intrusion detection: anomaly detection vs. classification. AISec 2013: 67-76 - [c26]Fabian Yamaguchi, Christian Wressnegger, Hugo Gascon, Konrad Rieck:
Chucky: exposing missing checks in source code for vulnerability discovery. CCS 2013: 499-510 - [c25]Christian Wressnegger, Frank Boldewin, Konrad Rieck:
Deobfuscating Embedded Malware Using Probable-Plaintext Attacks. RAID 2013: 164-183 - [e1]Konrad Rieck, Patrick Stewin, Jean-Pierre Seifert:
Detection of Intrusions and Malware, and Vulnerability Assessment - 10th International Conference, DIMVA 2013, Berlin, Germany, July 18-19, 2013. Proceedings. Lecture Notes in Computer Science 7967, Springer 2013, ISBN 978-3-642-39234-4 [contents] - 2012
- [j9]Konrad Rieck, Christian Wressnegger, Alexander Bikadorov:
Sally: a tool for embedding strings in vector spaces. J. Mach. Learn. Res. 13: 3247-3251 (2012) - [j8]Tammo Krueger, Konrad Rieck:
Intelligent Defense against Malicious JavaScript Code. Prax. Inf.verarb. Kommun. 35(1): 54-60 (2012) - [c24]Fabian Yamaguchi, Markus Lottmann, Konrad Rieck:
Generalized vulnerability extrapolation using abstract syntax trees. ACSAC 2012: 359-368 - [c23]Kristof Schütt
Early detection of malicious behavior in JavaScript code. AISec 2012: 15-24 - [c22]Tammo Krueger, Hugo Gascon, Nicole Krämer
Learning stateful models for network honeypots. AISec 2012: 37-48 - [c21]Guido Schwenk, Alexander Bikadorov, Tammo Krueger, Konrad Rieck:
Autonomous learning for detection of JavaScript attacks: vision or reality? AISec 2012: 93-104 - 2011
- [j7]Konrad Rieck:
Self-Learning Network Intrusion Detection. it Inf. Technol. 53(3): 152-156 (2011) - [j6]Konrad Rieck, Philipp Trinius, Carsten Willems, Thorsten Holz
Automatic analysis of malware behavior using machine learning. J. Comput. Secur. 19(4): 639-668 (2011) - [j5]Konrad Rieck:
Similarity measures for sequential data. WIREs Data Mining Knowl. Discov. 1(4): 296-304 (2011) - [c20]Marek Jawurek, Martin Johns
Smart metering de-pseudonymization. ACSAC 2011: 227-236 - [c19]Konrad Rieck:
Computer Security and Machine Learning: Worst Enemies or Best Friends? SysSec@DIMVA 2011: 107-110 - [c18]Guido Schwenk, Konrad Rieck:
Adaptive Detection of Covert Communication in HTTP Requests. EC2ND 2011: 25-32 - [c17]Fabian Yamaguchi, Felix "FX" Lindner, Konrad Rieck:
Vulnerability Extrapolation: Assisted Discovery of Vulnerabilities Using Machine Learning. WOOT 2011: 118-127 - 2010
- [j4]Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller:
Approximate Tree Kernels. J. Mach. Learn. Res. 11: 555-580 (2010) - [c16]Konrad Rieck, Tammo Krueger, Andreas Dewald:
Cujo: efficient detection and prevention of drive-by-download attacks. ACSAC 2010: 31-39 - [c15]Tammo Krueger, Nicole Krämer
ASAP: Automatic Semantics-Aware Analysis of Network Payloads. PSDML 2010: 50-63 - [c14]Tammo Krueger, Christian Gehl, Konrad Rieck, Pavel Laskov:
TokDoc: a self-healing web application firewall. SAC 2010: 1846-1853 - [c13]Konrad Rieck, Guido Schwenk, Tobias Limmer, Thorsten Holz
Botzilla: detecting the "phoning home" of malicious software. SAC 2010: 1978-1984 - [c12]Philipp Trinius, Carsten Willems, Thorsten Holz, Konrad Rieck:
A Malware Instruction Set for Behavior-Based Analysis. Sicherheit 2010: 205-216
2000 – 2009
- 2009
- [b1]Konrad Rieck:
Machine learning for application layer intrusion detection. Berlin Institute of Technology, 2009 - [j3]Stefan Wahl, Konrad Rieck, Pavel Laskov, Peter Domschitz, Klaus-Robert Müller
Securing IMS against novel threats. Bell Labs Tech. J. 14(1): 243-257 (2009) - [c11]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Active learning for network intrusion detection. AISec 2009: 47-54 - 2008
- [j2]Konrad Rieck, Pavel Laskov:
Linear-Time Computation of Similarity Measures for Sequential Data. J. Mach. Learn. Res. 9: 23-48 (2008) - [c10]Konrad Rieck, Thorsten Holz
Learning and Classification of Malware Behavior. DIMVA 2008: 108-125 - [c9]Patrick Düssel, Christian Gehl, Pavel Laskov, Konrad Rieck:
Incorporation of Application Layer Protocol Syntax into Anomaly Detection. ICISS 2008: 188-202 - [c8]Konrad Rieck, Stefan Wahl, Pavel Laskov, Peter Domschitz, Klaus-Robert Müller
A Self-learning System for Detection of Anomalous SIP Messages. IPTComm 2008: 90-106 - [c7]Thorsten Holz, Christian Gorecki, Konrad Rieck, Felix C. Freiling:
Measuring and Detecting Fast-Flux Service Networks. NDSS 2008 - [i2]Lothar Braun, Falko Dressler, Thorsten Holz, Engin Kirda, Jan Kohlrausch, Christopher Kruegel, Tobias Limmer, Konrad Rieck, James P. G. Sterbenz:
08102 Working Group -- Requirements for Network Monitoring from an IDS Perspective. Network Attack Detection and Defense 2008 - [i1]Marc Dacier, Hervé Debar, Thorsten Holz, Engin Kirda, Jan Kohlrausch, Christopher Kruegel, Konrad Rieck, James P. G. Sterbenz:
8102 Working Group -- Attack Taxonomy. Network Attack Detection and Defense 2008 - 2007
- [j1]Konrad Rieck, Pavel Laskov:
Language models for detection of unknown attacks in network traffic. J. Comput. Virol. 2(4): 243-256 (2007) - [c6]Pavel Laskov, Konrad Rieck, Klaus-Robert Müller:
Machine Learning for Intrusion Detection. NATO ASI Mining Massive Data Sets for Security 2007: 366-373 - 2006
- [c5]Konrad Rieck, Pavel Laskov, Klaus-Robert Müller
Efficient Algorithms for Similarity Measures over Sequential Data: A Look Beyond Kernels. DAGM-Symposium 2006: 374-383 - [c4]Konrad Rieck, Pavel Laskov:
Detecting Unknown Network Attacks Using Language Models. DIMVA 2006: 74-90 - [c3]Konrad Rieck, Pavel Laskov, Sören Sonnenburg:
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees. NIPS 2006: 1177-1184 - 2005
- [c2]Pavel Laskov, Patrick Düssel, Christin Schäfer, Konrad Rieck:
Learning Intrusion Detection: Supervised or Unsupervised?. ICIAP 2005: 50-57 - [c1]Pavel Laskov, Konrad Rieck, Christin Schäfer, Klaus-Robert Müller:
Visualization of anomaly detection using prediction sensitivity. Sicherheit 2005: 197-208
Coauthor Index
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