Fos-R
Related software
- FlowChronicle (flow pattern learning)
- TADAM (packets sequence learning)
- Fos-R (generation)
Related publications
On synthetic data quality:
- Schoen, A., Blanc, G., Gimenez, P. F., Han, Y., Majorczyk, F., & Mé, L. (2024). A Tale of Two Methods: Unveiling the limitations of GAN and the Rise of Bayesian Networks for Synthetic Network Traffic Generation. In Proceedings of the 9th International Workshop on Traffic Measurements for Cybersecurity (WTMC 2024).
On flow pattern learning:
- Cüppers, J., Schoen, A., Blanc, G. & Gimenez, P. F., (2024, December). FlowChronicle: Synthetic Network Flow Generation through Pattern Set Mining Generation. In the 20th International Conference on emerging Networking EXperiments and Technologies (CoNEXT).
On packets sequence learning:
- Cornanguer, L. & Gimenez, P. F., (2025 May). TADAM: Learning Timed Automata From Noisy Observations. In the SIAM International Conference on Data Mining (SDM25).
Scientific contributors
- Inria: Pierre-François Gimenez, Yufei Han, Ludovic Mé, Adrien Schoen
- CISPA: Lénaïg Cornanguer, Joscha Cüppers
- Télécom SudParis: Grégory Blanc
- DGA: Frédéric Majorczyk
Software contributors
- Inria: Pierre-François Gimenez, Adrien Schoen
- CISPA: Lénaïg Cornanguer, Joscha Cüppers
- CentraleSupélec: Evan Morin, Florentin Labelle