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HAMMERSCHMIDT Christian

Main Referenced Co-authors
STATE, Radu  (14)
Verwer, Sicco (7)
CAMINO, Ramiro Daniel  (4)
LAGRAA, Sofiane  (3)
Pellegrino, Gaetano (3)
Main Referenced Keywords
Statistics - Machine Learning (3); botnet (2); Computer Science - Learning (2); intrusion detection (2); regression (2);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (18)

Publications (total 18)

The most downloaded
1249 downloads
Verwer, S. E., & HAMMERSCHMIDT, C. (2017). flexfringe: A Passive Automaton Learning Package. In Software Maintenance and Evolution (ICSME), 2017 IEEE International Conference on. doi:10.1109/ICSME.2017.58 https://hdl.handle.net/10993/32814

The most cited

33 citations (Scopus®)

LAGRAA, S., François, J., Lahmadi, A., Minier, M., HAMMERSCHMIDT, C., & STATE, R. (2017). BotGM: Unsupervised Graph Mining to Detect Botnets in Traffic Flows. In CSNet 2017 Conference Proceedings. https://hdl.handle.net/10993/36519

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (17 July 2020). Working with Deep Generative Models and Tabular Data Imputation [Paper presentation]. First Workshop on the Art of Learning with Missing Values (Artemiss), Vienna, Austria.

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (2020). Minority Class Oversampling for Tabular Data with Deep Generative Models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/43194.

DU, M., HAMMERSCHMIDT, C., VARISTEAS, G., STATE, R., BRORSSON, M. H., & Zhang, Z. (2019). Time Series Modeling of Market Price in Real-Time Bidding. In 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Peer reviewed

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (2019). Improving Missing Data Imputation with Deep Generative Models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/43196.

KAIAFAS, G., HAMMERSCHMIDT, C., LAGRAA, S., & STATE, R. (2019). An Experimental Analysis of Fraud Detection Methods in Enterprise Telecommunication Data using Unsupervised Outlier Ensembles. In G. KAIAFAS, C. HAMMERSCHMIDT, ... R. STATE, 16th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2019). Piscataway, United States - New York: Institute of Electrical and Electronics Engineers.
Peer reviewed

KAIAFAS, G., HAMMERSCHMIDT, C., LAGRAA, S., & STATE, R. (2019). Auto Semi-supervised Outlier Detection for Malicious Authentication Events. ECML PKDD 2019 Workshops. doi:10.1007/978-3-030-43887-6_14
Peer reviewed

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (July 2018). Generating Multi-Categorical Samples with Generative Adversarial Networks [Paper presentation]. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden.

HAMMERSCHMIDT, C. (2017). Learning Finite Automata via Flexible State-Merging and Applications in Networking [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/33624

HAMMERSCHMIDT, C., Garcia, S., Verwer, S., & STATE, R. (October 2017). Reliable Machine Learning for Networking: Key Concerns and Approaches [Poster presentation]. The 42nd IEEE Conference on Local Computer Networks (LCN), Singapore, Singapore.

Verwer, S. E., & HAMMERSCHMIDT, C. (2017). flexfringe: A Passive Automaton Learning Package. In Software Maintenance and Evolution (ICSME), 2017 IEEE International Conference on. doi:10.1109/ICSME.2017.58
Peer reviewed

HAMMERSCHMIDT, C., STATE, R., & Verwer, S. (August 2017). Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms [Poster presentation]. Human in the Loop Machine Learning Workshop at the International Conference on Machine Learning, Sydney, Australia.

LAGRAA, S., François, J., Lahmadi, A., Minier, M., HAMMERSCHMIDT, C., & STATE, R. (2017). BotGM: Unsupervised Graph Mining to Detect Botnets in Traffic Flows. In CSNet 2017 Conference Proceedings.
Peer reviewed

HAMMERSCHMIDT, C., Verwer, S., Lin, Q., & STATE, R. (2016). Interpreting Finite Automata for Sequential Data. Interpretable Machine Learning for Complex Systems: NIPS 2016 workshop proceedings.
Peer reviewed

HAMMERSCHMIDT, C., Marchal, S., Pellegrino, G., STATE, R., & Verwer, S. (November 2016). Efficient Learning of Communication Profiles from IP Flow Records [Poster presentation]. The 41st IEEE Conference on Local Computer Networks (LCN).

HAMMERSCHMIDT, C., Marchal, S., STATE, R., & Verwer, S. (October 2016). Behavioral Clustering of Non-Stationary IP Flow Record Data [Poster presentation]. 12th International Conference on Network and Service Management.

Pellegrino, G., HAMMERSCHMIDT, C., Lin, Q., & Verwer, S. (October 2016). Learning Deterministic Finite Automata from Infinite Alphabets [Paper presentation]. The 13th International Conference on Grammatical Inference.

HAMMERSCHMIDT, C., LOOS, B. L., Verwer, S., & STATE, R. (October 2016). Flexible State-Merging for learning (P)DFAs in Python [Paper presentation]. The 13th International Conference on Grammatical Inference.

Lin, Q., HAMMERSCHMIDT, C., Pellegrino, G., & Verwer, S. (2016). Short-term Time Series Forecasting with Regression Automata [Poster presentation]. ACM SIGKDD 2016 Workshop on Mining and Learning from Time Series (MiLeTS).

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