Suchparameter:
  • Suche in Namens-Datensätzen nach: "wurzenberger" ("wurzenberger" als Name interpretiert)
  • Suche eingeschränkt auf Publikationsarten: Alle
  • Suche eingeschränkt auf Zeitraum: Alle Daten in der Datenbank
Mit Ihren Suchparametern wurden 55 passende Datensätze gefunden:
55 - Center "Digital Safety & Security"




Bücher und Buch-Herausgaben:


  1. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer (Hrg.):
    "Smart Log Data Analytics";
    Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6; 208 S.


Zeitschriftenbeiträge:


  1. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner:
    "Kyoushi Testbed Environment: A Model-driven Simulation Framework to Generate Open Log Data Sets for Security Evaluations";
    ERCIM News, 129 (2022), 24; S. 24 - 25.

  2. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner, A. Rauber:
    "Have It Your Way: Generating Customized Log Data Sets with a Model-driven Simulation Testbed";
    Transactions on Reliability, 70 (2021), S. 402 - 415.

  3. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, A. Rauber:
    "Dealing with Security Alert Flooding: Using Machine Learning for Domain-independent Alert Aggregation";
    ACM Transactions on Privacy and Security, 25 (2022), 18; S. 1 - 36.

  4. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, A. Rauber:
    "System Log Clustering Approaches for Cyber Security Applications: A Survey";
    Computers & Security, - (2020), 92; S. 1 - 17.

  5. Quelle: Center "Digital Safety & Security"

    M. Landauer, M. Wurzenberger, F. Skopik, G. Settanni, P. Filzmoser:
    "Dynamic Log File Analysis: An Unsupervised Cluster Evolution Approach for Anomaly Detection";
    Computers & Security, 79 (2018), S. 94 - 116.

  6. Quelle: Center "Digital Safety & Security"

    G. Settanni, F. Skopik, M. Wurzenberger, R. Fiedler:
    "Countering Targeted Cyber Attacks in Industry 4.0 through Anomaly Detection for Self-Adapting CPS";
    Elektrotechnik & Informationstechnik, 135 (2018), 3; S. 278 - 285.

  7. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Landauer, M. Wurzenberger:
    "Online Log Data Analysis With Efficient Machine Learning: A Review";
    IEEE Security & Privacy, 20 (2022), 2022.03; S. 80 - 90.

  8. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Landauer, M. Wurzenberger, G. Vormayr, J. Milosevic, J. Fabini, W. Prüggler, O. Kruschitz, B. Widmann, K. Truckenthanner, S. Rasse, M. Simmer, C. Zauner:
    "synERGY: Cross-correlation of operational and contextual data to timely detect and mitigate attacks to cyber-physical systems";
    Journal of Information Security and Applications, 54 (2020), S. 1 - 23.

  9. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, R. Fiedler:
    "synERGY: Detecting advanced attacks across multiple layers of cyber-physical systems";
    ERCIM News, 114 (2018), S. 30 - 31.

  10. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "DECEPT: Detecting Cyber-Physical Attacks using Machine Learning on Log Data";
    ERCIM News, 123 (2020), S. 33 - 34.

  11. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "The Seven Golden Principles of Effective Anomaly-Based Intrusion Detection";
    IEEE Security & Privacy, 19 (2021), S. 36 - 45.

  12. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik:
    "The BAESE Testbed - Analytic Evaluation of IT Security Tools in Specified Network Environments";
    ERCIM News, 107 (2016), S. 51 - 52.

  13. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik:
    "The BÆSE Testbed - Analytic Evaluation of IT Security Tools in Specified Network Environments";
    ERCIM News, 107 (2016), S. 51 - 52.

  14. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, G. Settanni, W. Scherrer:
    "Complex Log File Synthesis for Rapid Sandbox-Benchmarking of Security- and Computer Network Analysis Tools";
    Information Systems, 60 (2016), S. 13 - 33.


Buchbeiträge:


  1. Quelle: Center "Digital Safety & Security"

    I. Friedberg, M. Wurzenberger, A. Al Balushi, B. Kang:
    "From Monitoring, Logging, and Network Analysis to Threat Intelligence Extraction";
    in: "Collaborative Cyber Threat Intelligence", CRC Press, Abingdon, 2017, ISBN: 978-1138031821, S. 69 - 128.

  2. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "A Concept for a Tree-Based Log Parser Generator";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 131 - 149.

  3. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "AECID: A Light-Weight Log Analysis Approach for Online Anomaly Detection";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 99 - 129.

  4. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Detecting Unknown Cyber Security Attacks Through System Behavior Analysis";
    in: "Cybersecurity of Digital Service Chains", 13300; J. Kołodziej, M. Repetto, A. Duzha (Hrg.); herausgegeben von: Springer; Springer, 2022, ISBN: 978-3-031-04036-8, S. 103 - 119.

  5. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Final Remarks";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 173.

  6. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Generating Character-Based Templates for Log Data";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 63 - 81.

  7. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Incremental Log Data Clustering for Processing Large Amounts of Data Online";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 43 - 61.

  8. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Introduction";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 1 - 11.

  9. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Survey on Log Clustering Approaches";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 13 - 41.

  10. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Time Series Analysis for Temporal Anomaly Detection";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 83 - 98.

  11. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Variable Type Detector for Statistical Analysis of Log Tokens";
    in: "Smart Log Data Analytics", Springer Nature, Cham, Schweiz, 2021, ISBN: 978-3-030-74449-6, S. 151 - 171.

  12. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, M. Landauer, A. Bajraktari, F. Skopik:
    "Automatic Attack Pattern Mining for Generating Actionable CTI Applying Alert Aggregation";
    in: "Cybersecurity of Digital Service Chains", 13300; J. Kołodziej, M. Repetto, A. Duzha (Hrg.); herausgegeben von: Springer; Springer, 2022, ISBN: 978-3-031-04036-8, S. 136 - 161.

  13. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, G. Settanni:
    "Big Data for Cybersecurity";
    in: "Encyclopedia of Big Data Technologies", Springer, Cham, 2018, ISBN: 978-3-319-63962-8, S. 1 - 9.


Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):


  1. Quelle: Center "Digital Safety & Security"

    I. Friedberg, S. McLaughlin, P. Smith, M. Wurzenberger:
    "Towards a Resilience Metric Framework for Cyber-Physical Systems";
    Vortrag: 4th International Symposium for ICS & SCADA Cyber Security Research 2016, Belfast; 23.08.2016 - 25.08.2016; in: "4th International Symposium for ICS & SCADA Cyber Security Research 2016", BCS, (2016), ISSN: 1477-9358; S. 19 - 22.

  2. Quelle: Center "Digital Safety & Security"

    M. Landauer, M. Frank, F. Skopik, M. Wurzenberger, A. Rauber:
    "A Framework for Automatic Labeling of Log Datasets from Model-driven Testbeds for HIDS Evaluation";
    Vortrag: ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, 27.04.2022; in: "Proceedings of the 2022 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems", Association for Computing Machinery, New York, NY, USA, (2022), ISBN: 9781450392297; S. 77 - 86.

  3. Quelle: Center "Digital Safety & Security"

    M. Landauer, G. Höld, M. Wurzenberger, F. Skopik, A. Rauber:
    "Iterative Selection of Categorical Variables for Log Data Anomaly Detection";
    Vortrag: 26th European Symposium on Research in Computer Security, Darmstadt, Germany, October 4-8, 2021, Darmstadt; 04.10.2021 - 08.10.2021; in: "Proceedings of the 26th European Symposium on Research in Computer Security", Springer, 26 (2021), ISBN: 978-3-030-88417-8; S. 757 - 777.

  4. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner, A. Rauber:
    "A Framework for Cyber Threat Intelligence Extraction from Raw Log Data";
    Vortrag: International Conference on Big Data, Los Angeles; 09.12.2019 - 12.12.2019; in: "Proceedings of the 2019 IEEE International Conference on Big Data (Big Data)", IEEE, (2019), ISBN: 978-1-7281-0858-2; S. 3200 - 3209.

  5. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner, A. Rauber:
    "Have It Your Way: Generating Customized Log Data Sets with a Model-driven Simulation Testbed";
    Vortrag: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS), Macau; 11.12.2020 - 14.12.2020; in: "Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)", IEEE, (2020), S. 52.

  6. Quelle: Center "Digital Safety & Security"

    M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner, A. Rauber:
    "Visualizing Syscalls using Self-Organizing Maps for System Intrusion Detection";
    Poster: ICISSP, Valletta; 25.02.2020 - 27.02.2020; in: "Proceedings of the 6th International Conference on Information Systems Security and Privacy", SciTePress, 1 (2020), ISBN: 978-989-758-399-5; S. 349 - 360.

  7. Quelle: Center "Digital Safety & Security"

    M. Landauer, M. Wurzenberger, F. Skopik, G. Settanni, P. Filzmoser:
    "Time Series Analysis: Unsupervised Anomaly Detection Beyond Outlier Detection";
    Vortrag: 14th International Conference on Information Security Practice and Experience (ISPEC 2018), Tokyo; 25.09.2018 - 27.09.2018; in: "Information Security Practice and Experience", Springer, Cham, (2018), ISBN: 978-3-319-99806-0; S. 19 - 36.

  8. Quelle: Center "Digital Safety & Security"

    G. Settanni, Y. Shovgenya, F. Skopik, M. Wurzenberger, R. Graf, R. Fiedler:
    "Acquiring Cyber Threat Intelligence through Security Information Correlation";
    Vortrag: 3rd IEEE International Conference on Cybernetics (CYBCONF-2017) WS/SS, Exeter - United Kingdom; 21.06.2017 - 23.06.2017; in: "2017 3rd IEEE International Conference on Cybernetics (CYBCONF)", IEEE eXpress Conference Publishing, (2017), ISBN: 978-1-5386-2201-8; S. 415 - 421.

  9. Quelle: Center "Digital Safety & Security"

    G. Settanni, F. Skopik, A. Karaj, M. Wurzenberger, R. Fiedler:
    "Protecting Cyber Physical Production Systems using Anomaly Detection to enable Self-adaptation";
    Vortrag: 1st IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2018), Saint Petersburg; 15.05.2018 - 18.05.2018; in: "USB Proceedings 2018 IEEE Industrial Cyber-Physical Systems (ICPS)", IEEE, (2018), ISBN: 978-1-5386-6530-5; S. 173 - 180.

  10. Quelle: Center "Digital Safety & Security"

    G. Settanni, F. Skopik, M. Wurzenberger, R. Graf, R. Fiedler:
    "Correlating Cyber Incident Information to Establish Situational Awareness in Critical Infrastructures";
    Vortrag: Fourteenth annual conference on Privacy, Security and Trust, Auckland - New Zeland; 12.12.2016 - 14.12.2016; in: "Proceedings of the 14th International Conference on Privacy, Security and Trust", IEEE, http://ieeexplore.ieee.org/document/7906940/ (2016), ISBN: 978-1-5090-4379-8; S. 78 - 81.

  11. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, G. Settanni, R. Fiedler:
    "Establishing Cyber Situational Awareness through Incident Information Clustering";
    Vortrag: International Conference on Cyber Situational Awareness Data Analytics and Assessment, London, UK; 08.06.2015 - 09.06.2015; in: "International Conference on Cyber Situational Awareness Data Analytics and Assessment", Cyril Onwubiko, London (2015), ISBN: 978-0-9932338-0-7; S. 300 - 314.

  12. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, G. Höld, M. Landauer, F. Skopik, W. Kastner:
    "Creating Character-based Templates for Log Data to Enable Security Event Classification";
    Vortrag: ASIA CCS '20: The 15th ACM Asia Conference on Computer and Communications Security, Taipei; 05.10.2020 - 09.10.2020; in: "ASIA CCS '20: Proceedings of the 15th ACM Asia Conference on Computer and Communications Security", ACM, (2020), ISBN: 978-1-4503-6750-9; S. 141 - 152.

  13. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, M. Landauer, F. Skopik, W. Kastner:
    "AECID-PG: A Tree-Based Log Parser Generator To Enable Log Analysis";
    Vortrag: IFIP/IEEE IM 2019 Workshop: 4th IEEE/IFIP International Workshop on Analytics for Network and Service Management, Washington; 08.04.2019; in: "IFIP/IEEE IM 2019 Workshop: 4th IEEE/IFIP International Workshop on Analytics for Network and Service Management", (2019), ISBN: 978-3-903176-15-7; S. 7 - 12.

  14. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, R. Fiedler, W. Kastner:
    "Applying High-Performance Bioinformatics Tools for Outlier Detection in Log Data";
    Vortrag: 3rd IEEE International Conference on Cybernetics (CYBCONF-2017) WS/SS, Exeter - United Kingdom; 21.06.2017 - 23.06.2017; in: "2017 3rd IEEE International Conference on Cybernetics (CYBCONF)", IEEE eXpress Conference Publishing, (2017), ISBN: 978-1-5386-2201-8; S. 399 - 406.

  15. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, R. Fiedler, W. Kastner:
    "Discovering Insider Threats from Log Data with High-Performance Bioniformatics Tools";
    Vortrag: MIST' 16, Wien; 28.10.2016; in: "MIST' 16 Proceedings of the 2016 International Workshop on Managing Inisider Security Threats", ACM, New York (2016), ISBN: 9781450345712; S. 109 - 112.

  16. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, M. Landauer, P. Greitbauer, R. Fiedler, W. Kastner:
    "Incremental Clustering for Semi-Supervised Anomaly Detection applied on Log Data";
    Vortrag: ARES - International Conference on Availability, Reliability and Security, Reggio Calabria; 29.08.2017 - 01.09.2017; in: "Proceedings of the 12th International Conference on Availability, Reliability and Security", The Association for Computing Machinery, New York, (2017), ISBN: 978-1-4503-5257-4; S. 1 - 6.

  17. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, G. Settanni, R. Fiedler:
    "AECID: A Self-learning Anomaly Detection Approach Based on Light-weight Log Parser Models";
    Vortrag: 4th International Conference on Information Systems Security and Privacy(ICISSP 2018), Funchal, Portugal; 22.01.2018 - 24.01.2018; in: "Proceedings of the 4th International Conference on Information Systems Security and Privacy, 2018", SCITEPRESS digital library, 2018, (2018), ISBN: 978-989-758-282-0; S. 386 - 397.

  18. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, F. Skopik, G. Settanni, R. Fiedler:
    "Beyond Gut Instincts: Understanding, Rating and Comparing Self-Learning ICT Intrusion Detecion Systems";
    Poster: International Conference on Cyber Situational Awareness Data Analytics and Assessment, London, UK; 08.06.2015 - 09.06.2015; in: "International Conference on Cyber Situational Awareness Data Analytics and Assessment", Cyril Onwubiko, London (2015), ISBN: 978-0-9932338-0-7; S. 205 - 207.


Vorträge und Posterpräsentationen (ohne Tagungsband-Eintrag):


  1. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger:
    "Cyber Security Research Program: Overview & Insights";
    Vortrag: ViSP System Security Research Meetup, Online (eingeladen); 10.12.2021.

  2. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Don't get hacked, get AMiner! Log Data Analysis for Intrusion Detection";
    Vortrag: In-Depth Security Conference Europe (DeepSec) 2021, Vienna (eingeladen); 18.11.2021 - 19.11.2021.

  3. Quelle: Center "Digital Safety & Security"

    F. Skopik, M. Wurzenberger, M. Landauer:
    "Machine Learning für Logdatenanalyse - Ein Ausblick auf Morgen";
    Vortrag: IKT Sicherheitskonferenz 2019, Fürstenfeld (eingeladen); 01.10.2019 - 02.10.2019.

  4. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger:
    "How Can AI Improve Cyber Situational Awareness?";
    Vortrag: European Big Data Value Forum, Wien (eingeladen); 12.11.2018 - 14.11.2018.

  5. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, M. Landauer:
    "Applying Machine Learning for System Log Data Analysis";
    Vortrag: European Security and Defence College (ESDC), Infrastructures in the Context of Digitization Course - ICD (2019-2020/254/1), Wien (eingeladen); 16.10.2019 - 18.10.2019.

  6. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger, M. Landauer:
    "ÆCID: A Self-Learning Anomaly Detection Approach Based on Light-weight Log Analytics";
    Vortrag: BSides Vienna 2019, Wien; 30.11.2019.


Dissertationen (eigene und begutachtete):


  1. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger:
    "Resource-Efficient Log Analysis to Enable Online Anomaly Detection in Cyber Security";
    Betreuer/in(nen), Begutachter/in(nen): W. Kastner, F. Skopik; Technische Universität Wien, 2021; Rigorosum: 26.03.2021.


Diplom- und Master-Arbeiten (eigene und betreute):


  1. Quelle: Center "Digital Safety & Security"

    M. Landauer:
    "Dynamic Log File Analysis: An Unsupervised Cluster Evolution Approach for Anomaly Detection";
    Betreuer/in(nen): P. Filzmoser, F. Skopik, M. Wurzenberger; TU Wien, Fakultät für Informatik, 2018; Abschlussprüfung: 11.04.2018.

  2. Quelle: Center "Digital Safety & Security"

    M. Wurzenberger:
    "Synthetic Log Data Modeling for the Evaluation of Intrusion Detection Systems";
    Betreuer/in(nen): W. Scherrer, F. Skopik; TU Wien, 2015; Abschlussprüfung: 24.11.2015.