E-LIB Suche - Ergebnisse für: Hammerschmidt, Christian

1

The Robust Malware Detection Challenge and Greedy Random Ac..:

, In: Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security,

Verwer, Sicco ; Nadeem, Azqa ; Hammerschmidt, Christian... - p. 61-70 , 2020

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

2

Auto Semi-supervised Outlier Detection for Malicious Authen..:

, In: Machine Learning and Knowledge Discovery in Databases; Communications in Computer and Information Science,

Kaiafas, Georgios ; Hammerschmidt, Christian ; Lagraa, Sofiane. - p. 176-190 , 2020

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

3

Beyond Labeling: Using Clustering to Build Network Behavior..:

, In: Malware Analysis Using Artificial Intelligence and Deep Learning,

Nadeem, Azqa ; Hammerschmidt, Christian ; Gañán, Carlos H.. - p. 381-409 , 2020

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

4

Behavioral Clustering of Non-Stationary IP Flow Record Data:

, In: Proceedings of the 12th Conference on International Conference on Network and Service Management,

Hammerschmidt, Christian ; Marchal, Samuel ; State, Radu. - p. 297-301 , 2016

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

5

„Gaudeamus igitur?“ – Prävalenz und Einflussfaktoren auf d..:

Hammerschmidt, Christian , 2011

Link: https://nbn-resolving.de..

6

FlexFringe: Modeling Software Behavior by Learning Probabil..:

Verwer, Sicco ; Hammerschmidt, Christian
http://arxiv.org/abs/2203.16331. , 2022

Link: http://arxiv.org/abs/220..

7

Die einkommensteuerliche Gewinnermittlung doppelstöckiger M..:

Hammerschmidt, Christian
https://opus.bsz-bw.de/hsf/frontdoor/index/index/docId/1876. , 2021

Link: https://opus.bsz-bw.de/h..

8

Federated Learning For Cyber Security: SOC Collaboration Fo..:

Khramtsova, Ekaterina ; Hammerschmidt, Christian ; Lagraa, Sofiane.
https://orbilu.uni.lu/handle/10993/44927. , 2020

Link: https://orbilu.uni.lu/ha..

9

Minority Class Oversampling for Tabular Data with Deep Gene..:

Camino, Ramiro ; Hammerschmidt, Christian ; State, Radu
http://arxiv.org/abs/2005.03773. , 2020

Link: http://arxiv.org/abs/200..

10

Working with Deep Generative Models and Tabular Data Imputa..:

Camino, Ramiro Daniel ; Hammerschmidt, Christian ; State, Radu
https://orbilu.uni.lu/handle/10993/44101. , 2020

Link: https://orbilu.uni.lu/ha..

11

Beyond Labeling: Using Clustering to Build Network Behavior..:

Nadeem, Azqa ; Hammerschmidt, Christian ; Gañán, Carlos H.
http://arxiv.org/abs/1904.01371. , 2019

Link: http://arxiv.org/abs/190..

12

Improving Missing Data Imputation with Deep Generative Mode..:

Camino, Ramiro D ; Hammerschmidt, Christian A ; State, Radu
http://arxiv.org/abs/1902.10666. , 2019

Link: http://arxiv.org/abs/190..

13

Generating Multi-Categorical Samples with Generative Advers..:

Camino, Ramiro ; Hammerschmidt, Christian ; State, Radu
http://arxiv.org/abs/1807.01202. , 2018

Link: http://arxiv.org/abs/180..

14

Generating Multi-Categorical Samples with Generative Advers..:

Camino, Ramiro Daniel ; Hammerschmidt, Christian ; State, Radu
https://orbilu.uni.lu/handle/10993/36084. , 2018

Link: https://orbilu.uni.lu/ha..

15

flexfringe: A Passive Automaton Learning Package:

Verwer, Sicco E ; Hammerschmidt, Christian
FNR10053360 - Stream Mining For Predictive Authentication Under Adversarial Influence, 2015 (01/03/2015-14/11/2017) - Christian Hammerschmidt. , 2017

Link: https://orbilu.uni.lu/ha..