Publications

In Water distribution systems analysis (WDSA)/computing and control water industry (CCWI) joint conference

Abstract Water distribution infrastructures are increasingly incorporating IoT in the form of sensing and computing power to improve control over the system and achieve a greater adaptability to the water demand. This evolution, from physical towards cyberphysical systems, comes with an attack perimeter extended to the cyberspace. Being able to detect this novel kind of attacks is gaining traction in the scientific community. However, machine learning detection algorithms, which are showing encouraging results in cybersecurity applications, needs training data as close as possible to real world data in order to perform well in production environment.

Translation of semi-extended regular expressions using derivatives

By Antoine Martin, Etienne Renault, Alexandre Duret-Lutz

2024-06-20

In Proceedings of the 28th international conference on implementation and applications of automata (CIAA’24)

Abstract We generalize Antimirov’s notion of linear form of a regular expression, to the Semi-Extended Regular Expressions typically used in the Property Specification Language or SystemVerilog Assertions. Doing so requires extending the construction to handle more operators, and dealing with expressions over alphabets $\Sigma=2^{AP}$ of valuations of atomic propositions. Using linear forms to construct automata labeled by Boolean expressions suggests heuristics that we evaluate. Finally, we study a variant of this translation to automata with accepting transitions: this construction is more natural and provides smaller automata.

Transforming gradient-based techniques into interpretable methods

By Caroline Mazini-Rodriguez, Nicolas Boutry, Laurent Najman

2024-06-01

In Pattern Recognition Letters

Abstract The explication of Convolutional Neural Networks (CNN) through xAI techniques often poses challenges in interpretation. The inherent complexity of input features, notably pixels extracted from images, engenders complex correlations. Gradient-based methodologies, exemplified by Integrated Gradients (IG), effectively demonstrate the significance of these features. Nevertheless, the conversion of these explanations into images frequently yields considerable noise. Presently, we introduce GAD (Gradient Artificial Distancing) as a supportive framework for gradient-based techniques. Its primary objective is to accentuate influential regions by establishing distinctions between classes.

Graph-based spectral analysis for detecting cyber attacks

By Majed Jaber, Nicolas Boutry, Pierre Parrend

2024-05-01

In ARES 2024 (the international conference on availability, reliability and security)

Abstract Spectral graph theory delves into graph properties through their spectral signatures. The eigenvalues of a graph’s Laplacian matrix are crucial for grasping its connectivity and overall structural topology. This research capitalizes on the inherent link between graph topology and spectral characteristics to enhance spectral graph analysis applications. In particular, such connectivity information is key to detect low signals that betray the occurrence of cyberattacks. This paper introduces SpectraTW, a novel spectral graph analysis methodology tailored for monitoring anomalies in network traffic.

The Quickref cohort

By Didier Verna

2024-05-01

In ELS 2024, the 17th european lisp symposium

Abstract The internal architecture of Declt, our reference manual generator for Common Lisp libraries, is currently evolving towards a three-stage pipeline in which the information gathered for documentation purposes is first reified into a formalized set of object-oriented data structures. A side-effect of this evolution is the ability to dump that information for other purposes than documentation. We demonstrate this ability applied to the complete Quicklisp ecosystem. The resulting “cohort” includes more than half a million programmatic definitions, and can be used to gain insight into the morphology of Common Lisp software.

Weakly supervised training for hologram verification in identity documents

By Glen Pouliquen, Guillaume Chiron, Joseph Chazalon, Thierry Géraud, Ahmad Montaser Awal

2024-04-25

In The 18th international conference on document analysis and recognition (ICDAR 2024)

Abstract We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as “holograms”, in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples.

An end-to-end approach for the detection of phishing attacks

By Badis Hammi, Tristan Billot, Danyil Bazain, Nicolas Binand, Maxime Jaen, Chems Mitta, Nour El Madhoun

2024-04-01

In Advanced information networking and applications (AINA))

Abstract The main approaches/implementations used to counteract phishing attacks involve the use of crowd-sourced blacklists. However, blacklists come with several drawbacks. In this paper, we present a comprehensive approach for the detection of phishing attacks. Our approach uses our own detection engine which relies on Graph Neural Networks to leverage the hyperlink structure of the websites to analyze. Additionally, we offer a turnkey implementation to the end-users in the form of a Mozilla Firefox plugin.

Automatic vectorization of historical maps: A benchmark

By Yizi Chen, Joseph Chazalon, Edwin Carlinet, Minh Ôn Vũ Ngọc, Clément Mallet, Julien Perret

2024-04-01

In PLOS ONE

Abstract Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps.

Unsupervised discovery of interpretable visual concepts

By Caroline Mazini-Rodrigues, Nicolas Boutry, Laurent Najman

2024-01-14

In Information Sciences

Abstract Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model’s decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts.