E-LIB Suche - Ergebnisse für: Landauer, Max

1

Anomaly detection in log-event sequences: A federated deep ..:

Himler, Patrick ; Landauer, Max ; Skopik, Florian.
Machine Learning with Applications. 16 (2024) - p. 100554 , 2024

Link: https://doi.org/10.1016/..

2

Analysis of statistical properties of variables in log data..:

Wurzenberger, Markus ; Höld, Georg ; Landauer, Max.
Computers & Security. 137 (2024) - p. 103631 , 2024

Link: https://doi.org/10.1016/..

3

NEWSROOM: Towards Automating Cyber Situational Awareness Pr..:

, In: Proceedings of the 19th International Conference on Availability, Reliability and Security,

Wurzenberger, Markus ; Krenn, Stephan ; Landauer, Max... - p. 1-11 , 2024

Link: https://dl.acm.org/doi/1..

4

On the Application of Natural Language Processing for Advan..:

, In: Proceedings of the 19th International Conference on Availability, Reliability and Security,

Skopik, Florian ; Akhras, Benjamin ; Woisetschläger, Elisabeth... - p. 1-10 , 2024

Link: https://dl.acm.org/doi/1..

5

A logging maturity and decision model for the selection of ..:

Kern, Manuel ; Landauer, Max ; Skopik, Florian.
Computers & Security. 141 (2024) - p. 103844 , 2024

Link: https://doi.org/10.1016/..

6

Behavior-Based Anomaly Detection in Log Data of Physical Ac..:

Skopik, Florian ; Wurzenberger, Markus ; Höld, Georg..
IEEE Transactions on Dependable and Secure Computing. 20 (2023) 4 - p. 3158-3175 , 2023

Link: https://doi.org/10.1109/..

7

Maintainable Log Datasets for Evaluation of Intrusion Detec..:

Landauer, Max ; Skopik, Florian ; Frank, Maximilian...
IEEE Transactions on Dependable and Secure Computing. 20 (2023) 4 - p. 3466-3482 , 2023

Link: https://doi.org/10.1109/..

8

Deep learning for anomaly detection in log data: A survey:

Landauer, Max ; Onder, Sebastian ; Skopik, Florian.
Machine Learning with Applications. 12 (2023) - p. 100470 , 2023

Link: https://doi.org/10.1016/..

9

Towards Detecting Anomalies in Log-Event Sequences with Dee..:

, In: Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference,

Himler, Patrick ; Landauer, Max ; Skopik, Florian. - p. 71-77 , 2023

Link: https://dl.acm.org/doi/1..

10

AMiner: A Modular Log Data Analysis Pipeline for Anomaly-ba..:

Landauer, Max ; Wurzenberger, Markus ; Skopik, Florian..
Digital Threats: Research and Practice. 4 (2023) 1 - p. 1-16 , 2023

Link: https://doi.org/10.1145/..

11

Strategic selection of data sources for cyber attack detect..:

, In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing,

Kern, Manuel ; Skopik, Florian ; Landauer, Max. - p. 1656-1665 , 2022

Link: https://dl.acm.org/doi/1..

12

A User and Entity Behavior Analytics Log Data Set for Anoma..:

, In: 2022 IEEE International Conference on Big Data (Big Data),

Landauer, Max ; Skopik, Florian ; Hold, Georg. - p. 4285-4294 , 2022

Link: https://doi.org/10.1109/..

13

A Framework for Automatic Labeling of Log Datasets from Mod..:

, In: Proceedings of the 2022 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems,

Landauer, Max ; Frank, Maximilian ; Skopik, Florian... - p. 77-86 , 2022

Link: https://dl.acm.org/doi/1..

14

Detecting Unknown Cyber Security Attacks Through System Beh..:

, In: Cybersecurity of Digital Service Chains; Lecture Notes in Computer Science,

Skopik, Florian ; Wurzenberger, Markus ; Landauer, Max - p. 103-119 , 2022

Link: https://doi.org/10.1007/..

15

Blind Spots of Security Monitoring in Enterprise Infrastruc..:

Skopik, Florian ; Landauer, Max ; Wurzenberger, Markus
IEEE Security & Privacy. 20 (2022) 6 - p. 18-26 , 2022

Link: https://doi.org/10.1109/..