Christian A. Hammerschmidt
Christian A. Hammerschmidt
Other namesChristian Albert Hammerschmidt, Chris A. Hammerschmidt
APTA Technologies B.V.
Verified email at - Homepage
Cited by
Cited by
Generating multi-categorical samples with generative adversarial networks
R Camino, C Hammerschmidt, R State
arXiv preprint arXiv:1807.01202, 2018
Improving missing data imputation with deep generative models
RD Camino, R Hammerschmidt, CA, State
arXiv preprint arXiv:1902.10666, 2019
BotGM: Unsupervised graph mining to detect botnets in traffic flows
S Lagraa, J Franšois, A Lahmadi, M Miner, C Hammerschmidt, R State
Cyber Security in Networking Conference (CSNet), 2017 1st, 1-8, 2017
flexfringe: A Passive Automaton Learning Package
SE Verwer, C Hammerschmidt
Software Maintenance and Evolution (ICSME), 2017 IEEE Internationalá…, 2017
Radu State. Improving missing data imputation with deep generative models
RD Camino, CA Hammerschmidt
arXiv preprint arXiv:1902.10666, 2019
Learning behavioral fingerprints from Netflows using Timed Automata
G Pellegrino, Q Lin, C Hammerschmidt, S Verwer
Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium oná…, 2017
Behavioral clustering of non-stationary IP flow record data
C Hammerschmidt, S Marchal, R State, S Verwer
Network and Service Management (CNSM), 2016 12th International Conference oná…, 2016
Short-term time series forecasting with regression automata
Q Lin, C Hammerschmidt, G Pellegrino, S Verwer
Federated learning for cyber security: SOC collaboration for malicious URL detection
E Khramtsova, C Hammerschmidt, S Lagraa, R State
2020 IEEE 40th International Conference on Distributed Computing Systemsá…, 2020
Efficient Learning of Communication Profiles from IP Flow Records
C Hammerschmidt, S Marchal, R State, G Pellegrino, S Verwer
Local Computer Networks (LCN), 2016 IEEE 41st Conference on, 559-562, 2016
Interpreting Finite Automata for Sequential Data
CA Hammerschmidt, S Verwer, Q Lin, R State
arXiv preprint arXiv:1611.07100, 2016
Beyond labeling: Using clustering to build network behavioral profiles of malware families
A Nadeem, C Hammerschmidt, CH Ga˝ßn, S Verwer
Malware analysis using artificial intelligence and deep learning, 381-409, 2021
Reliable Machine Learning for Networking: Key Issues and Approaches
CA Hammerschmidt, S Garcia, S Verwer, R State
Local Computer Networks (LCN), 2017 IEEE 42nd Conference on, 167-170, 2017
The robust malware detection challenge and greedy random accelerated multi-bit search
S Verwer, A Nadeem, C Hammerschmidt, L Bliek, A Al-Dujaili, ...
Proceedings of the 13th ACM Workshop on Artificial Intelligence and Securityá…, 2020
Working with deep generative models and tabular data imputation
RD Camino, C Hammerschmidt
First Workshop on the Art of Learning with Missing Values (Artemiss), 2020
An experimental analysis of fraud detection methods in enterprise telecommunication data using unsupervised outlier ensembles
G Kaiafas, C Hammerschmidt, ...
IEEE Symposium on Integrated Network and Service Management (IM), 37-42, 2019
Learning deterministic finite automata from infinite alphabets
G Pellegrino, C Hammerschmidt, Q Lin, S Verwer
International Conference on Grammatical Inference, 120-131, 2017
Flexfringe: Modeling software behavior by learning probabilistic automata
S Verwer, C Hammerschmidt
arXiv preprint arXiv:2203.16331, 2022
Oversampling tabular data with deep generative models: Is it worth the effort?
RD Camino, CA Hammerschmidt
PMLR, 2020
Auto Semi-supervised Outlier Detection for Malicious Authentication Events
G Kaiafas, C Hammerschmidt, S Lagraa, R State
Joint European Conference on Machine Learning and Knowledge Discovery iná…, 2019
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