Pierre Parrend

In Rencontre des jeunes chercheurs en inteligence artificielle (RJCIA-2023)

Abstract At this time, cyberattacks represent a constant threat. Many approaches exist for detecting suspicious behaviors, but very few of them seem to benefit from the huge potential of mathematical approaches like spectral graph analysis, known to be able to extract topological features of a graph using its Laplacian spectrum. For this reason, we consider our network as a dynamic graph composed of nodes (representing the devices) and of edges (representing the requests), and we compute its Laplacian spectrum across time.

CRACS: Compaction of rules in anticipatory classifier systems

By Romain Orhand, Pierre Collet, Pierre Parrend, Anne Jeannin-Girardon

2023-06-01

In Proceedings of the companion conference on genetic and evolutionary computation

Abstract Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned.

Metrics for community dynamics applied to unsupervised attacks detection

By Julien Michel, Pierre Parrend

2023-06-01

In Rencontres des jeunes chercheurs en intelligence artificielle

Abstract Attack detection in big networks has become a necessity. Yet, with the ever changing threat landscape and massive amount of data to handle, network intrusion detection systems (NIDS) end up being obsolete. Different machine-learning-based solutions have been developed to answer the detection problem for data with evolving statistical distributions. However, no approach has proved to be both scalable and robust to passing time. In this paper, we propose a scalable and unsupervised approach to detect behavioral patterns without prior knowledge on the nature of attacks.

GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver reported information on 237 individuals with Koolen-de Vries syndrome

By Florent Colin, Pauline Burger, Timothée Mazzucotelli, Axelle Strehle, Joost Kummeling, Nicole Collot, Elyette Broly, Angela Morgan, Kenneth Myers, Agnès Bloch-Zupan, Charlotte Ockeloen, Bert de Vries, Tjitske Kleefstra, David Koolen, Pierre Parrend, Jean-Louis Mandel

2023-05-01

In Genetics in Medicine Open

Abstract

Metrics for evaluating interface explainability models for cyberattack detection in IoT data

By Amani Abou Rida, Rabih Amhaz, Pierre Parrend

2023-04-01

In Complex computational ecosystems 2023 (CCE’23)

Abstract The importance of machine learning (ML) in detecting cyberattacks lies in its ability to efficiently process and analyze large volumes of IoT data, which is critical in ensuring the security and privacy of sensitive information transmitted between connected devices. However, the lack of explainability of ML algorithms has become a significant concern in the cybersecurity community. Therefore, explainable techniques are developed to make ML algorithms more transparent, thereby improving trust in attack detection systems by its ability to allow cybersecurity analysts to understand the reasons for model predictions and to identify any limitation or error in the model.

Towards attack detection in traffic data based on spectral graph analysis

By Majed Jaber, Nicolas Boutry, Pierre Parrend

2023-04-01

In

Abstract Nowadays, cyberattacks have become a significant concern for individuals, organizations, and governments. These attacks can take many forms, and the consequences can be severe. In order to protect ourselves from these threats, it is essential to employ a range of different strategies and techniques like detection of patterns, classification of system behaviors against previously known attacks, and anomaly detection techniques. This way, we can identify unknown forms of attacks. Few of these existing techniques seem to fully utilize the potential of mathematical approaches such as spectral graph analysis.

GenIDA: An international participatory database to gain knowledge on health issues related to genetic forms of neurodevelopmental disorders

By Pauline Burger, Florent Colin, Romain Coutelle, Axelle Strehle, Timothée Mazzucotelli, Nicole Collot, Benjamin Durand, Arianne Bouman, Daphna Landau Prat, Tjitske Kleefstra, Pierre Parrend, Amélie Piton, David Koolen, Jean-Louis Mandel

2022-11-01

In Journal of Neural Transmission

Abstract Intellectual disability with or without manifestations of autism and/or epilepsy affects 1-2% of the population, and it is estimated that more than 30-50% of these cases have a single genetic cause. More than 1000 genes and recurrent chromosomal abnormalities are involved in these genetic forms of neurodevelopmental disorders, which often remain insufficiently described in terms of clinical spectrum, associated medical problems, etc., due to their rarity and the often-limited number of patients’ phenotypes reported.

A hybrid optimization tool for active magnetic regenerator

By Anna Ouskova Leonteva, Michel Risser, Radia Hamane, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

2022-07-01

In Proceedings of the genetic and evolutionary computation conference companion

Abstract Active Magnetic Regenerator (AMR) refrigeration is an innovate technology, which can reduce energy consumption and the depletion of the ozone layer. However, to develop a commercially applicable design of the AMR model is still an issue, because of the difficulty to find a configuration of the AMR parameters, which are suitable for various applications needs. In this work, we focus on the optimization method for finding a common parameters of the AMR model in two application modes: a magnetic refrigeration system and a thermo-magnetic generator.

Anomaly detection on static and dynamic graphs using graph convolutional neural networks

By Amani Abou Rida, Rabih Amhaz, Pierre Parrend

2022-03-01

In Robotics and AI for cybersecurity and critical infrastructure in smart cities

Abstract Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) [1]. Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently.

GenIDA, une base de données participative internationale permettant de mieux connaître l’histoire naturelle et les comorbidités des formes génétiques de troubles neurodéveloppementaux

By Jean-Louis Mandel, Pauline Burger, Axelle Strehle, Florent Colin, Timothée Mazzucotelli, Nicole Collot, S. Baer, Benjamin Durand, Amélie Piton, Romain Coutelle, Elise Schaefer, Pierre Parrend, L. Faivre, K. Jobard Garou, D. Geneviève, V. Ruault, D. Martin, R. Caumes, T. Smol, J. Ghoumid, F. Ropert Conquer, J. Kummeling, C. Ockeloen, Tjitske Kleefstra, David Koolen

2022-02-01

In Assises de génétique humaine et médicale

Abstract

Structural and spectral analysis of dynamic graphs for attack detection

CRACS: Compaction of rules in anticipatory classifier systems

Metrics for community dynamics applied to unsupervised attacks detection

GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver reported information on 237 individuals with Koolen-de Vries syndrome

Metrics for evaluating interface explainability models for cyberattack detection in IoT data

Towards attack detection in traffic data based on spectral graph analysis

GenIDA: An international participatory database to gain knowledge on health issues related to genetic forms of neurodevelopmental disorders

A hybrid optimization tool for active magnetic regenerator

Anomaly detection on static and dynamic graphs using graph convolutional neural networks

GenIDA, une base de données participative internationale permettant de mieux connaître l’histoire naturelle et les comorbidités des formes génétiques de troubles neurodéveloppementaux

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