Christophe Rodrigues

@inproceedings{estignard_3247,

title = {Relevance of Imaged-based Representation for Android Malware Detection},

author = {Foucauld Estignard and Adrien Djebar and Hugo Deduit and Sourav Rai and Christophe Rodrigues and Adam Talbi and Nga Nguyen},

editor = {ACM DIGITAL LIBRARY},

url = {https://crisis2024.univ-gustave-eiffel.fr/},

issn = {978-3-031-89349-0},

year  = {2025},

date = {2025-05-01},

booktitle = {The 19th International Conference on Risks and Security of Internet and Systems},

pages = {543 - 557},

publisher = {Springer-VerlagBerlin, Heidelberg},

address = {Aix-en-Provence, France},

organization = {Risks and Security of Internet and Systems: 19th International Conference, CRiSIS 2024},

abstract = {In the face of escalating cybersecurity threats targeting Android devices, developing accurate and explainable malware detection methodologies has become increasingly crucial.

This paper studies the relevance of using image representation of bytecode for Android malware detection by introducing a new metric, the Median Color Proximity (MCP).

The MCP quantifies the degree to which applications with similar behaviors are represented by visually similar images, which we argue is a key prerequisite for using image-based representation of Android Application Packages (APK).

We conducted our analysis on a dataset of 29,555 APK files, employing Uniform Maniform Approximation and Projection (UMAP) for clustering on both their behavior and their visual representation.

Our findings reveal a significant correlation between pixel distribution and application behavior, suggesting that image-based representations effectively capture the essential behavioral characteristics of APKs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zahhar_3624,

title = {Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval},

author = {Saber Zahhar and Nédra Mellouli and Christophe Rodrigues},

url = {https://link.springer.com/chapter/10.1007/978-981-96-1483-7_8},

issn = {978-981-96-1482-0},

year  = {2025},

date = {2025-02-01},

booktitle = {International Conference on Web Information Systems Engineering},

volume = {15463},

pages = {101-110},

publisher = {Springer, Singapore},

address = {Doha, Qatar},

abstract = {Meeting the Sustainable Development Goals (SDGs) established by the United Nations, presents a large-scale challenge for all countries. To monitor progress towards these goals, there is a need to develop key performance indicators using existing data and metadata. The computation of the indicators requires integrating and analyzing heterogeneous datasets, in particular web open data. This approach aims to highlight the positive impact of the web on the society. However, the diversity of web data sources and formats raises major issues in terms of structuring and integration. Despite the abundance of open data and metadata, its exploitation remains limited, leaving untapped potential for guiding urban policies towards sustainability. We have so far introduced a novel approach for SDG indicator computation, leveraging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs). We have proposed a method that combines rule-based filtering with LLM-powered schema mapping to establish semantic correspondences between diverse data sources and SDG indicators, including disaggregated attributes. Our approach integrated these mappings into a KG, which enables indicator computation by querying graph's topology. Finally, we have evaluated our method through a case study focusing on the SDG Indicator 11.7.1 about accessibility of public open spaces. Our experimental results are promising showing significant improvements compared to traditional schema matching techniques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{talbi_3246,

title = {Detecting Obfuscated Android Malware through Categorized Smali N-gram Instructions and Ensemble Learning},

author = {Adam Talbi and Sourav Rai and Christophe Rodrigues and Nga Nguyen},

url = {https://crisis2024.univ-gustave-eiffel.fr/},

issn = {Later},

year  = {2024},

date = {2024-11-01},

booktitle = {The 19th International Conference on Risks and Security of Internet and Systems},

address = {Aix-en-Provence, France},

abstract = {Malware detection is a critical task in ensuring the security of mobile devices. However, the growing prevalence of obfuscated malware poses challenges to traditional detection methods. In this study, we propose a new approach to detecting obfuscated malware based on Smali bytecode analysis, instruction categorization into families, N-gram vectorization and machine learning techniques. We collected a dataset comprising 8,000 obfuscated APKs from VirusTotal, with half labeled as malicious and half as benign. The dataset includes recent samples from 2024, ensuring the relevance of our results. We extracted the Smali bytecode from each APK, categorized the instructions into families, then transformed them into ordered sequences of n-grams. Our method relies on the use of ensemble learning algorithms to combine the strengths of various models. In particular, we used Random Forest and XGBoost in combination with an n-gram vectorization process of categorized Smali instructions. This approach enabled us to achieve an average F1-score of 0.96 (± 0.01) in cross-validation and a precision of 0.98 for detecting malware and 0.94 for detecting benign software on the test set, with an average recall of 0.96, the overall F1 score also reached 0.96. 

Our approach, which integrates Smali bytecode analysis, instruction family categorization, and ensemble learning, has demonstrated promising results in detecting obfuscated malware. These findings underscore the importance of a detailed analysis of Smali bytecode and the effectiveness of ensemble learning algorithms in identifying obfuscated malware.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ortega_2654,

title = {REDIRE : Réduction Extrême de DImension pour le Résumé Extractif},

author = {Marius Ortega and Nédra Mellouli and Aurélien Bossard and Christophe Rodrigues},

url = {https://iutdijon.u-bourgogne.fr/egc2024/articles-acceptes/},

year  = {2024},

date = {2024-01-01},

booktitle = {24ème conférence francophone sur l'Extraction et la Gestion des Connaissances},

address = {Dijon, France},

abstract = {This paper presents an unsupervised automatic summarization model capable of extracting the most important sentences from a corpus. To extract sentences in a summary, we use

pre-entrained word embeddings to represent the documents. From this thick cloud of word vectors,

we apply an extreme dimension reduction to identify important words, which we group

by proximity. Sentences are extracted using linear optimization to maximize the information

present in the summary. We evaluate the approach on large documents and present very encouraging initial results.},

note = {22-26/01/2024},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{haikal_1767,

title = {Place Embedding across Cities in Location-based Social Networks},

author = {Christophe Haikal and Pegah Alizadeh and Christophe Rodrigues and Bi Chongke},

url = {https://www.sigapp.org/sac/sac2022/file2022/TOC-Jan-23-2022.pdf},

year  = {2022},

date = {2022-04-01},

booktitle = {ACM Symposium on Applied Computing},

address = {Brno, Czech Republic},

abstract = {In the urban computing field, analysing human mobility patterns are used to understand tourists and residents behaviours, or urban areas functionalities. Transferring the information from one city to another also assists urban strategists in comparing two cities

and applying their knowledge from one to another, such as plan- ning new touristic attractions or implementing some successful urban strategies in new cities. For this goal, in this work, we propose a twofold semi-supervised approach: we first propose a place embedding method based on users mobility trajectories in each city individually, second we learn a place translation function between two cities using the set of reviews written for each place. The translation function is based on measuring textual and semantic

similarities of the reviews. Extensive experiments on two french metropolitan areas (Lille and Bordeaux) based on the extracted data from Trip-Advisor demonstrate the effectiveness of our approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{belemkoabga_1586,

title = {Neural Network-Based Generation of Sport Summaries: a Preliminary Study},

author = {David Stéphane Belemkoabga and Aurélien Bossard and Abdallah Essa and Christophe Rodrigues and Kévin Sylla},

url = {https://ranlp.org/ranlp2021/proceedings.pdf},

year  = {2021},

date = {2021-07-01},

booktitle = {Proceedings of Recent Advances in Natural Language Processing},

pages = {152-159},

address = {Varna, Bulgaria},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{osmani_1543,

title = {Equational Model Guided by Real-time Sensor Data to Monitor Industrial Robots},

author = {Aomar Osmani and Pegah Alizadeh and Christophe Rodrigues},

url = {https://case2021.sciencesconf.org/},

year  = {2021},

date = {2021-06-01},

booktitle = {International Conference on Automation Science and Engineering},

address = {Lyon, France},

abstract = {The monitoring of industrial robots is often en-

sured by generic simulators which model the equational aspect

of the target machines. We propose an original approach to

complete the equational simulator of a milling machine using

the accumulated data from the used sensors. This approach cre-

ates a specific simulator for each machining situation by taking

the triplet (material, cutting tool, workpiece) into account. This

improvement brings great added value to the industrial experts

and improves the efficiency of industrial robots. It allows them

to better follow and interpret the behavior of machines during

the milling process. In addition to correct the simulator using

real data, our method detects also the anomalies during the

real manufacturing performance and fixes the minor bugs

along the observed real data during its continuous simulation

mimicry. The additional interest of our model remains the

precise definition of the complementary model between the real

system and the equational simulator. This makes it possible, by

using an inductive approach to search for regularities in the

model in order to better interpret the structural differences

between the model and the system and to better understand

the situations linked to their functionalities or undesirable

situations. The intensive experiments on real data validate our

model and open up many perspectives for future works.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nyzam_521,

title = {Un outil modulaire pour le résumé automatique},

author = {Valentin Nyzam and Christophe Rodrigues and Aurélien Bossard},

url = {https://project.inria.fr/coriataln2018/fr/},

year  = {2018},

date = {2018-05-01},

booktitle = {TALN 2018},

address = {Rennes, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodrigues_282,

title = {An Evolutionary Algorithm for Automatic Summarization},

author = {Christophe Rodrigues},

url = {https://www.aclweb.org/anthology/volumes/R17-1/},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of Recent Advances in Natural Language Processing},

pages = {111-120},

address = {Varna, Bulgaria},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}