TADAM: Learning Timed Automata From Noisy Observations
Cornanguer, L. & Gimenez, P. F., (2025 May). TADAM: Learning Timed Automata From Noisy Observations. In the SIAM International Conference on Data Mining (SDM25).
CV
Recent positions
- Since 2024: Research scientist in cybersecurity and artificial intelligence
- Inria (Rennes, France)
- Intrusion detection and security data generation
- Also part-time professor at CentraleSupélec
- 2020–2024: Cybersecurity chair as an associate professor
- CentraleSupélec and Inria (Rennes, France)
- Intrusion detection and malware analysis
- 2018–2020: Postdoc in cybersecurity
- LAAS-CNRS (Toulouse, France)
- Intrusion detection based on machine learning
- 2015–2018: PhD in artificial intelligence
- IRIT (Toulouse, France)
- Preferences learning and application to e-commerce
Experience
Research projects
I am a member of several “PEPR Cybersécurité” projects:
I am the Inria’s PI (principal investigator) of the SecGen associate team with CISPA. This project is dedicated to synthetic data generation for intrusion detection systems.
Doctoral advisor
I am currently co-supervising 4 PhD students:
- Vincent Raulin on dynamic malware analysis (with Valérie Viet Triem Tong and Yufei Han)
- Grégor Quetel on intrusion detection system by semantics inference based on instrumented parsers (with Laurent Pautet, Thomas Robert and Eric Alata)
- Fanny Dijoud on intrusion detection system from both applications logs and network traffic packets (with Michel Hurfin, Frédéric Majorczyk and Barbara Pilastre)
- Solène Delourme, on automatic detection and response in SOC with generative AI (with Jean-François Lalande, Tony Quertier and Colin Leverger)
Alumni PhD:
- Maxime Lanvin on unsupervised intrusion detection in networks (with Ludovic Mé, Grégory Blanc, Yufei Han and Frédéric Majorczyk), defended in 2024
- Adrien Schoen on network packets generation for IDS evaluation (with Ludovic Mé, Éric Totel, Yufei Han and Frédéric Majorczyk), defended in 2024
I was also involved in various PhD: Malcolm Bourdon (defended in 2021), Aliénor Damien (defended in 2020) and Jonathan Roux (defended in 2020).
Teaching activity
I am a part-time professor at CentraleSupélec. You can find more details about my teaching here.
Other collaborations
I was a member of the working group that lead to the ANSSI publication Building trust in AI through a cyber risk-based approach.
Expert assessments
I occasionally serve as an expert for ANRT (Association Nationale de la Recherche et de la Technologie) and PTCC (Programme de Transfert au Campus Cyber).
Scientific events and dissemination
- “Hands-on machine learning for security” event, sponsored by the “GDR Sécurité Informatique”
- Seminars of the CIDRE team (https://team.inria.fr/cidre/research/seminars/) since 2020 (about 50 talks) and of the PIRAT team (https://team.inria.fr/pirat/pirat-biweekly-seminars/) since 2024
- Doctoral paper reading group “Papers Please” for CIDRE (https://team.inria.fr/cidre/papers-please/) since 2022 and for PIRAT (https://team.inria.fr/pirat/papers-please/) since 2024
- Youtube page of the team (https://www.youtube.com/@pirat-research-team) since 2020
- Various scientific events in the team
PhD defense jury member
- Hamdi Friji (2024, Télécom SudParis)
- Léo Lavaur (2024, IMT Atlantique)
- Mohamed El Bouazzati (2023, Université Bretagne Sud)
- Malcolm Bourdon (2021, LAAS-CNRS)
PC member and reviewer
Security conferences
- RESSI 2025
- THCon 2022, 2023, 2024, 2025
- ERTS 2024
- EICC 2021, 2023
- DSN 2022
- Doctoral Forum of DSN 2020
- ISSRE 2020
Artificial intelligence conferences
- ARTMAN 2023, 2024
- ECAI 2023
- AAAI 2021, 2022
- IJCAI 2020
- RJCIA 2019, 2020
Journals
- IEEE Network
- Transactions on Computers
- International Journal of Information Security
- Computers & Security
- Journal of Computer Security
Publications
FlowChronicle: Synthetic Network Flow Generation through Pattern Set Mining
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).
Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle
Gimenez, P. F., & Mengin, J. (2024). Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
A Tale of Two Methods: Unveiling the limitations of GAN and the Rise of Bayesian Networks for Synthetic Network Traffic Generation
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).
A Parser-Based Data Collector for Intrusion Detection
Grégor Quétel, Eric Alata, Pierre-François Gimenez, Laurent Pautet, Thomas Robert. A Parser-Based Data Collector for Intrusion Detection. Rendez-Vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information, May 2024, Eppe-Sauvage, France.
Survey on system-level graph-based and anomaly-based intrusion detection
Dijoud, F., Gimenez, P. F., Hurfin, M., Majorczyk, F., & Pilastre, B. (2024, May). Survey on system-level graph-based and anomaly-based intrusion detection. In RESSI 2024-Rendez-Vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information (pp. 1-2).
Towards Understanding Alerts raised by Semi-supervised Network Intrusion Detection Systems
Maxime Lanvin, Pierre-François Gimenez, Yufei Han, Frédéric Majorczyk, Ludovic Mé, et al.. Towards Understanding Alerts raised by Unsupervised Network Intrusion Detection Systems. The 26th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2023), Oct 2023, Hong Kong, France. ⟨10.1145/3607199.3607247⟩
Conditionally Acyclic CO-Networks for Efficient Preferential Optimization
Gimenez, Pierre-François & Mengin, Jérôme. (2023). Conditionally Acyclic CO-Networks for Efficient Preferential Optimization. 10.3233/FAIA230352.
BAGUETTE: Hunting for evidence of malicious behavior in dynamic analysis reports
Vincent Raulin, Pierre-François Gimenez, Yufei Han, Valérie Viet Triem Tong. BAGUETTE: Hunting for Evidence of Malicious Behavior in Dynamic Analysis Reports. 20th International conference on security and cryptography SECRYPT 2023, Jul 2023, Rome, Italy.
Errors in the CICIDS2017 dataset and the significant differences in detection performances it makes
Lanvin, M., Gimenez, P. F., Han, Y., Majorczyk, F., Mé, L., & Totel, E. (2022, December). Errors in the CICIDS2017 dataset and the significant differences in detection performances it makes. In CRiSIS 2022-International Conference on Risks and Security of Internet and Systems.
Explainable artificial intelligence for cybersecurity: a literature survey
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez, Yufei Han, Houda Jmila, Gregory Blanc, Takeshi Takahashi & Zonghua Zhang. Explainable artificial intelligence for cybersecurity: a literature survey. Ann. Telecommun. (2022).
The complexity of unsupervised learning of lexicographic preferences
Fargier, H., Gimenez, P. F., Mengin, J., & Le Nguyen, B. N. (2022, July). The complexity of unsupervised learning of lexicographic preferences. In 13th Multidisciplinary Workshop on Advances in Preference Handling (M-pref 2022)@ IJCAI 2022
Debiasing Android Malware Datasets: How can I trust your results if your dataset is biased?
Miranda, T. C., Gimenez, P. F., Lalande, J. F., Tong, V. V. T., & Wilke, P. (2022). Debiasing Android Malware Datasets: How can I trust your results if your dataset is biased?. IEEE Transactions on Information Forensics and Security.
Towards a Representation of Malware Execution Traces for Experts and Machine Learning
Raulin, V., Gimenez, P. F., Han, Y., & Tong, V. V. T. (2022). Towards a Representation of Malware Execution Traces for Experts and Machine Learning. RESSI 2022-Rendez-Vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information.
Detecting APT through graph anomaly detection
Lanvin, M., Gimenez, P. F., Han, Y., Majorczyk, F., Mé, L., & Totel, É. (2022, May). Detecting APT through graph anomaly detection. In RESSI 2022-Rendez-Vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information.
Towards generic quality assessment of synthetic traffic for evaluating intrusion detection systems
Schoen, A., Blanc, G., Gimenez, P. F., Han, Y., Majorczyk, F., & Mé, L. (2022, May). Towards generic quality assessment of synthetic traffic for evaluating intrusion detection systems. In RESSI 2022-Rendez-Vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information.
GUI-Mimic, a cross-platform recorder and fuzzer of Graphical User Interface
Raulin, V., Gimenez, P. F., Han, Y., Viet Triem Tong, V., Ouairy, L. (2021). GUI-Mimic, a cross-platform recorder and fuzzer of Graphical User Interface. 9th GreHack Conference
RIDS: Radio intrusion detection and diagnosis system for wireless communications in smart environment
Gimenez, P. F., Roux, J., Alata, E., Auriol, G., Kaâniche, M., & Nicomette, V. (2021). RIDS: Radio intrusion detection and diagnosis system for wireless communications in smart environment. ACM Transactions on Cyber-Physical Systems, 5(3), 1-1.
Hardware-Performance-Counters-based anomaly detection in massively deployed smart industrial devices
Bourdon, M., Gimenez, P. F., Alata, E., Kaaniche, M., Migliore, V., Nicomette, V., & Laarouchi, Y. (2020, November). Hardware-Performance-Counters-based anomaly detection in massively deployed smart industrial devices. In 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) (pp. 1-8). IEEE.
On-board Diagnosis: A First Step from Detection to Prevention of Intrusions on Avionics Applications
Damien, A., Gimenez, P. F., Feyt, N., Nicomette, V., Kaâniche, M., & Alata, E. (2020, October). On-board Diagnosis: A First Step from Detection to Prevention of Intrusions on Avionics Applications. In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) (pp. 358-368). IEEE.
Experimental evaluation of three value recommendation methods in interactive configuration
H Fargier, PF Gimenez, J Mengin - Journal of Universal Computer Science, 2020
Apprentissage de préférences en espace combinatoire et application à la recommandation en configuration interactive
Pierre-François Gimenez. Apprentissage de préférences en espace combinatoire et application à la recommandation en configuration interactive. Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2018. Français. ⟨NNT : 2018TOU30182⟩. ⟨tel-02303275⟩
Learning lexicographic preference trees from positive examples
Fargier, H., Gimenez, P. F., & Mengin, J. (2018, April). Learning lexicographic preference trees from positive examples. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
Recommendation for product configuration: an experimental evaluation (Best paper award)
Fargier, H., Gimenez, P. F., & Mengin, J. (2016, September). Recommendation for product configuration: an experimental evaluation. In 18th International Configuration Workshop (CWS 2016) within CP 2016: 22nd International Conference on Principles and Practice of Constraint Programming (pp. pp-9).