Team

Dipl.-Ing. Robert Luh BSc

  • Lecturer
  • Department of Computer Science and Security
Location: D - Heinrich Schneidmadl-Straße 15

Study programmes

  • IT Security (BA)
  • Data Science and Business Analytics (BA)
  • Information Security (MA)

Departments

  • Computer Science and Security

Short profile

  • 1999-2004: Higher Technical School for Economics and Information Technologies Hollabrunn
  • 2008-2011: University of Applied Sciences St. Pölten, BSc IT Security
  • 2011-2013: University of Applied Sciences St. Pölten, Master (Dipl.-Ing.) Information Security
  • Since 2015: PhD student at De Montfort University, Leicester

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Selected Publications

Luh, R., Schramm, G., Wagner, M., Janicke, H., & Schrittwieser, S. (2018). SEQUIN: a grammar inference framework for analyzing malicious system behavior. Journal of Computer Virology and Hacking Techniques, 01–21. https://doi.org/10.1007/s11416-018-0318-x
Rauchberger, J., Schrittwieser, S., Dam, T., Luh, R., Buhov, D., Pötzelsberger, G., & Kim, H. (2018). The Other Side of the Coin: A Framework for Detecting and Analyzing Web-based Cryptocurrency Mining Campaigns. In Proceedings of the 13th International Conference on Availability, Reliability and Security. Hamburg, Deutschland: ACM.
Eresheim, S., Luh, R., & Schrittwieser, S. (2017). The Evolution of Process Hiding Techniques in Malware – Current Threats and Possible Countermeasures. Journal of Information Processing. https://doi.org/10.2197/ipsjjip.25.866
Luh, R., Schrittwieser, S., & Marschalek, S. (2017). LLR-based Sentiment Analysis for Kernel Event Sequences. Presented at the 31th International Conference on Advanced Information Networking and Applications, IEEE.
Luh, R., Schramm, G., Wagner, M., & Schrittwieser, S. (2017). Sequitur-based Inference and Analysis Framework for Malicious System Behavior. Presented at the First International Workshop on Formal Methods for Security Engineering.
Wagner, M., Sacha, D., Rind, A., Fischer, F., Luh, R., Schrittwieser, S., … Aigner, W. (2017). Visual Analytics: Foundations and Experiences in Malware Analysis. In L. B. Othmane, M. G. Jaatun, & E. Weippl (Eds.), Empirical Research for Software Security: Foundations and Experience (pp. 139–171). CRC/Taylor and Francis.

Projects

Publications

Types
From
To
Luh, R., & Eigner, O. (2019, January). Google Hacking. Presented at the Security Day, FH St. Pölten.
Luh, R. (2018, June). PenQuest: Attacker/Defender Educational Game. Presented at the Studiengangsbeirat, Fachhochschule St. Pölten.
Luh, R. (2018, January). From murder to malware: Digital forensics for treasure hunters. Presented at the FH Kiel, FH Kiel.
Schrittwieser, S., & Luh, R. (2018, April). Mord im Planetarium - Ein Ausflug in die Welt der Digitalen Forensik. Presented at the Volkshochschule Wien, Wien.
Luh, R. (2018, January). Mord auf der Festplatte: Ein Ausflug in die digitale Forensik. VHS Wien.
Luh, R. (2018, January). Malware Analysis. Presented at the De Montfort University Leicester, De Montfort University Leicester.
Luh, R., Schramm, G., Wagner, M., Janicke, H., & Schrittwieser, S. (2018). SEQUIN: a grammar inference framework for analyzing malicious system behavior. Journal of Computer Virology and Hacking Techniques, 01–21. https://doi.org/10.1007/s11416-018-0318-x
Luh, R. (2018). Fragen an die Wissenschaft: Warum werden Datenbestände immer größer? Niederösterreichische Nachrichten.
Rauchberger, J., Schrittwieser, S., Dam, T., Luh, R., Buhov, D., Pötzelsberger, G., & Kim, H. (2018). The Other Side of the Coin: A Framework for Detecting and Analyzing Web-based Cryptocurrency Mining Campaigns. In Proceedings of the 13th International Conference on Availability, Reliability and Security. Hamburg, Deutschland: ACM.
Luh, R., & Eresheim, S. (2018, January). Google Hacking. Presented at the Security Day, FH St. Pölten.
Luh, Robert, Temper, M., Tjoa, S., & Schrittwieser, S. (2018). APT RPG: Design of a Gamified Attacker/Defender Meta Model. In International Workshop on FORmal methods for Security Engineering.
Thür, N., Wagner, M., Schick, J., Niederer, C., Eckel, J., Luh, R., & Aigner, W. (2017). A Bigram Supported Generic Knowledge-Assisted Malware Analysis System: BiG2-KAMAS. In Proceedings of the 10th Forum Media Technology 2017 (pp. 107–115). St. Pölten: CEUR-WS.
Thür, N., Wagner, M., Schick, J., Niederer, C., Eckel, J., Luh, R., & Aigner, W. (2017). BiG2-KAMAS: Supporting Knowledge-Assisted Malware Analysis with Bi-Gram Based Valuation. In Poster of the 14th Workshop on Visualization for Cyber Security (VizSec). Phoenix, Arizona, USA.
Marschalek, S., Luh, R., & Schrittwieser, S. (2017). Endpoint Data Classification Using Markov Chains. In 2017 International Conference on Software Security and Assurance (ICSSA) (pp. 56–59). Altoona, PA: IEEE. https://doi.org/10.1109/ICSSA.2017.17
Eresheim, S., Luh, R., & Schrittwieser, S. (2017). The Evolution of Process Hiding Techniques in Malware – Current Threats and Possible Countermeasures. Journal of Information Processing. https://doi.org/10.2197/ipsjjip.25.866
Rauchberger, J., Luh, R., & Schrittwieser, S. (2017). Longkit - A Universal Framework for BIOS/UEFI Rootkits in System Management Mode. Presented at the Third International Conference on Information Systems Security and Privacy, Madeira, Portugal.
Luh, R., Schrittwieser, S., & Marschalek, S. (2017). LLR-based Sentiment Analysis for Kernel Event Sequences. Presented at the 31th International Conference on Advanced Information Networking and Applications, IEEE.
Luh, R., Schrittwieser, S., Janicke, H., & Marschalek, S. (2017). Design of an Anomaly-based Threat Detection & Explication System. Presented at the Third International Conference on Information Systems Security and Privacy, Madeira, Portugal.
Luh, R., Schrittwieser, S., Marschalek, S., Janicke, H., & Weippl, E. (2017). Design of an Anomaly-based Threat Detection & Explication System. Presented at the 22nd ACM Symposium on Access Control Models and Technologies (SACMAT), ACM. https://doi.org/10.1145/3078861.3084162
Luh, R., Schramm, G., Wagner, M., & Schrittwieser, S. (2017). Sequitur-based Inference and Analysis Framework for Malicious System Behavior. Presented at the First International Workshop on Formal Methods for Security Engineering.
Wagner, M., Sacha, D., Rind, A., Fischer, F., Luh, R., Schrittwieser, S., … Aigner, W. (2017). Visual Analytics: Foundations and Experiences in Malware Analysis. In L. B. Othmane, M. G. Jaatun, & E. Weippl (Eds.), Empirical Research for Software Security: Foundations and Experience (pp. 139–171). CRC/Taylor and Francis.
Luh, R., Marschalek, S., Kaiser, M., Janicke, H., & Schrittwieser, S. (2016). Semantics-aware detection of targeted attacks – A survey. Journal of Computer Virology and Hacking Techniques, 1–39. https://doi.org/10.1007/s11416-016-0273-3
Marschalek, S., Kaiser, M., Luh, R., & Schrittwieser, S. (2016). Empirical Malware Research through Observation of System Behaviour. In First Workshop on Empirical Research Methods in Information Security (pp. 467–469). ACM. https://doi.org/10.1145/2872518.2888609
Luh, R., Schrittwieser, S., & Marschalek, S. (2016). TAON: An Ontology-based Approach to Mitigating Targeted Attacks. Presented at the International Conference on Information Integration and Web-based Applications & Services (iiWAS), ACM.
Wagner, M., Fischer, F., Luh, R., Haberson, A., Rind, A., Keim, D. A., & Aigner, W. (2015). A Survey of Visualization Systems for Malware Analysis. In R. Borgo, F. Ganovelli, & I. Viola (Eds.), Eurographics Conference on Visualization (EuroVis) - STARs (pp. 105–125). Cagliari, Italy: The Eurographics Association. https://doi.org/10.2312/eurovisstar.20151114
Haslinger, D., & Luh, R. (2015, September). Alltagsspuren von dir und mir. Presented at the 6. Science Day.
Marschalek, S., Luh, R., Kaiser, M., & Schrittwieser, S. (2015). Classifying Malicious System Behavior using Event Propagation Trees. In Proceedings of the 17th International Con- ference on Information Integration and Web-based Applications Services (iiWAS2015).
Wagner, M., Aigner, W., Rind, A., Dornhackl, H., Kadletz, K., Luh, R., & Tavolato, P. (2014). Problem Characterization and Abstraction for Visual Analytics in Behavior-Based Malware Pattern Analysis. In L. Harrison (Ed.), Proceedings of the Eleventh Workshop on Visualization for Cyber Security (pp. 9–16). Paris: ACM. https://doi.org/10.1145/2671491.2671498
Luh, R., & Tavolato, P. (2012). Behavior-Based Malware Recognition. In 6. Forschungsforum der Österreichischen Fachhochschulen - Tagungsband 1 Informationstechnologie als Produktionsfaktor (pp. 79–84). Graz, Österreich: Eigenverlag FH Joanneum GmbH.
Luh, R., & Tavolato, P. (2011). Automatische verhaltensbasierte Malware-Analyse. Hackin9, (11).