Département logique des usages, des sciences sociales et de l'information (LUSSI)

LUSSI est spécialisé dans les études, la modélisation et la conception de systèmes socio-techniques dans lesquels les données et l'humain jouent un rôle central.

Le département LUSSI se compose d'une équipe pluridisciplinaire au carrefour des Mathématiques, de l'Informatique et des Sciences Humaines et Sociales.

Recherche
Enseignement

Les prochains événements du département

04.06.2019 > 05.06.2019
Les dernières publications du département
Communication dans un congrès
Kabil Alexandre, Duval Thierry, Cuppens Nora, Le Comte Gérard, Halgand Yoran, Ponchel Christophe
From Cyber Security Activities to Collaborative Virtual Environments Practices through the 3D CyberCOP Platform
International Conference on Information Systems Security, Dec 2018, Bengaluru, India. pp.272-287, 2018, proceedings of ICISS 2018, 14th International Conference on Information Systems Security
Bibtext :
@inproceedings{kabil:hal-01892161,
TITLE = {{From Cyber Security Activities to Collaborative Virtual Environments Practices through the 3D CyberCOP Platform}},
AUTHOR = {Kabil, Alexandre and Duval, Thierry and Cuppens, Nora and Le Comte, G{\'e}rard and Halgand, Yoran and Ponchel, Christophe},
URL = {https://hal.archives-ouvertes.fr/hal-01892161},
BOOKTITLE = {{International Conference on Information Systems Security}},
ADDRESS = {Bengaluru, India},
SERIES = {proceedings of ICISS 2018, 14th International Conference on Information Systems Security},
PAGES = {272-287},
YEAR = {2018},
MONTH = Dec,
PDF = {https://hal.archives-ouvertes.fr/hal-01892161/file/main.pdf},
HAL_ID = {hal-01892161},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T From Cyber Security Activities to Collaborative Virtual Environments Practices through the 3D CyberCOP Platform
%+ Lab-STICC_IMTA_CID_IHSEV
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Lab-STICC_IMTA_CID_IRIS
%+ Département Systèmes Réseaux, Cybersécurité et Droit du numérique (SRCD)
%+ Société Générale
%+ EDF (EDF)
%+ Cassidian
%A Kabil, Alexandre
%A Duval, Thierry
%A Cuppens, Nora
%A Le Comte, Gérard
%A Halgand, Yoran
%A Ponchel, Christophe
%< avec comité de lecture
%B International Conference on Information Systems Security
%C Bengaluru, India
%3 proceedings of ICISS 2018, 14th International Conference on Information Systems Security
%P 272-287
%8 2018-12-17
%D 2018
%Z Computer Science [cs]/Graphics [cs.GR]
%Z Computer Science [cs]/Human-Computer Interaction [cs.HC]
%Z Computer Science [cs]/Cryptography and Security [cs.CR]Conference papers
%X Although collaborative practices between cyber organizations are well documented, managing activities within these organizations is still challenging as cyber operators tasks are very demanding and usually done individually. As human factors studies in cyber environments are still difficult to perform, tools and collaborative practices are evolving slowly and training is always required to increase teamwork efficiency. Contrary to other research fields, cyber security is not harnessing yet the capabilities of Collaborative Virtual Environments (CVE) which can be used both for immersive and interactive data visualization and serious gaming for training. In order to tackle cyber security teamwork issues, we propose a 3D CVE called the 3D Cyber Common Operational Picture, which aims at taking advantage of CVE practices to enhance cyber collaborative activities. Based on four Security Operations Centers (SOCs) visits we have made in different organizations, we have designed a cyber collaborative activity model which has been used as a reference to design our 3D CyberCOP platform features, such as asymetrical collaboration, mutual awareness and roles specialization. Our approach can be adapted to several use cases, and we are currently developing a cyber incident analysis scenario based on an event-driven architecture, as a proof of concept.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01892161/document
%2 https://hal.archives-ouvertes.fr/hal-01892161/file/main.pdf
%L hal-01892161
%U https://hal.archives-ouvertes.fr/hal-01892161
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ LAB-STICC_IMTA_CID_IHSEV
%~ IMT-ATLANTIQUE
%~ IMTA_SRCD
%~ LAB-STICC_IMTA_CID_IRIS
%~ EDF
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Communication dans un congrès
Dao Vinh-Loc, Bothorel Cécile, Lenca Philippe
Estimating the similarity of community detection methods based on cluster size distribution
Complex Networks 2018, The 7th International Conference on Complex Networks and Their Applications, Dec 2018, Cambridge, United Kingdom
Bibtext :
@inproceedings{dao:hal-01911077,
TITLE = {{Estimating the similarity of community detection methods based on cluster size distribution}},
AUTHOR = {Dao, Vinh-Loc and BOTHOREL, C{\'e}cile and Lenca, Philippe},
URL = {https://hal.archives-ouvertes.fr/hal-01911077},
BOOKTITLE = {{Complex Networks 2018, The 7th International Conference on Complex Networks and Their Applications}},
ADDRESS = {Cambridge, United Kingdom},
YEAR = {2018},
MONTH = Dec,
KEYWORDS = {community detection ; similarity metric ; community size ; comparative analysis},
PDF = {https://hal.archives-ouvertes.fr/hal-01911077/file/Cluster_size_distribution_comparisation_algos_detection_community_Dao_Bothorel_Lenca_2018.pdf},
HAL_ID = {hal-01911077},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Paper
%F Oral
%T Estimating the similarity of community detection methods based on cluster size distribution
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%A Dao, Vinh-Loc
%A BOTHOREL, Cécile
%A Lenca, Philippe
%< avec comité de lecture
%B Complex Networks 2018, The 7th International Conference on Complex Networks and Their Applications
%C Cambridge, United Kingdom
%8 2018-12-11
%D 2018
%K community detection
%K similarity metric
%K community size
%K comparative analysis
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Discrete Mathematics [cs.DM]
%Z Computer Science [cs]/Data Structures and Algorithms [cs.DS]
%Z Computer Science [cs]/Social and Information Networks [cs.SI]Conference papers
%X Detecting community structure discloses tremendous information about complex networks and unlock promising applied perspectives. Accordingly, a numerous number of community detection methods have been proposed in the last two decades with many rewarding discoveries. Notwithstanding, it is still very challenging to determine a suitable method in order to get more insights into the mesoscopic structure of a network given an expected quality, especially on large scale networks. Many recent efforts have also been devoted to investigating various qualities of community structure associated with detection methods, but the answer to this question is still very far from being straightforward. In this paper, we propose a novel approach to estimate the similarity between community detection methods using the size density distributions of communities that they detect. We verify our solution on a very large corpus of networks consisting in more than a hundred networks of five different categories and deliver pairwise similarities of 16 state-of-the-art and well-known methods. Interestingly, our result shows that there is a very clear distinction between the partitioning strategies of different community detection methods. This distinction plays an important role in assisting network analysts to identify their rule-of-thumb solutions.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01911077/document
%2 https://hal.archives-ouvertes.fr/hal-01911077/file/Cluster_size_distribution_comparisation_algos_detection_community_Dao_Bothorel_Lenca_2018.pdf
%L hal-01911077
%U https://hal.archives-ouvertes.fr/hal-01911077
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ TDS-MACS
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Article dans une revue
Simonin Jacques, Puentes John
Automatized integration of a contextual model into a process with data variability
Computer Languages, Systems & Structures, 2018, 54, pp.156 - 182. 〈10.1016/j.cl.2018.06.002〉
Bibtext :
@article{simonin:hal-01836388,
TITLE = {{Automatized integration of a contextual model into a process with data variability}},
AUTHOR = {Simonin, Jacques and Puentes, John},
URL = {https://hal.archives-ouvertes.fr/hal-01836388},
JOURNAL = {{Computer Languages, Systems \& Structures}},
HAL_LOCAL_REFERENCE = {19087},
VOLUME = {54},
PAGES = {156 - 182},
YEAR = {2018},
MONTH = Dec,
DOI = {10.1016/j.cl.2018.06.002},
KEYWORDS = {Data variability ; Contextual data ; Model transformation ; Substitution transformation ; Enhancement transformation},
HAL_ID = {hal-01836388},
HAL_VERSION = {v1},
}
Endnote :
%0 Journal Article
%T Automatized integration of a contextual model into a process with data variability
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Département lmage et Traitement Information (ITI)
%A Simonin, Jacques
%A Puentes, John
%< avec comité de lecture
%Z 19087
%J Computer Languages, Systems & Structures
%V 54
%P 156 - 182
%8 2018-12
%D 2018
%R 10.1016/j.cl.2018.06.002
%K Data variability
%K Contextual data
%K Model transformation
%K Substitution transformation
%K Enhancement transformation
%Z Computer Science [cs]/Computers and Society [cs.CY]
%Z Engineering Sciences [physics]/Signal and Image processing
%Z Computer Science [cs]/Modeling and SimulationJournal articles
%X Existent process models can hardly cope with the emerging issue of modelling exponential variable data volumes in systems' workflow, from specifications to operation. Given the strong relation between data context and data variability, this paper considers the automated integration of contextual models for processes with data variability. The proposed approach extends methodologically a platform independent model process, using a contextual data model, to obtain automatically the corresponding platform specific model. Contextual data are thus integrated to a process as a model, within a process. Two particular cases of contextual data models are studied in detail: substitution, when the contextual data model defines generated code, and enhancement, when learned data descriptions constitute the contextual data model. The feasibility and value of integrating a contextual model into a process to handle data variability are shown in detail describing these two use cases. Contextual model integration by substitution to include automatically variable ready to use application services to generate code, and contextual model integration by enhancement applied to supervised image classification based on variable descriptors. Results show that relating data variability and its context by means of automated integration of a designed system component model, simplifies variable data processing of system process models.
%G English
%L hal-01836388
%U https://hal.archives-ouvertes.fr/hal-01836388
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ TDS-MACS
%~ LAB-STICC
%~ UNIV-BREST
%~ IMT-ATLANTIQUE
%~ IMTA_LUSSI
%~ IMTA_ITI
%~ LAB-STICC_IMTA_CID_DECIDE
%~ LAB-STICC_IMTA
Communication dans un congrès
Khannoussi Arwa, Olteanu Alexandru Liviu, Dezan Catherine, Diguet Jean-Philippe, Labreuche Christophe, Petit-Frère Jacques, Meyer Patrick
Incremental Learning of Simple Ranking Method Using Reference Profiles Models
DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning, Nov 2018, Poznan, Poland
Bibtext :
@inproceedings{khannoussi:hal-01947860,
TITLE = {{Incremental Learning of Simple Ranking Method Using Reference Profiles Models}},
AUTHOR = {Khannoussi, Arwa and OLTEANU, Alexandru Liviu and Dezan, Catherine and Diguet, Jean-Philippe and Labreuche, Christophe and Petit-Fr{\`e}re, Jacques and Meyer, Patrick},
URL = {https://hal.archives-ouvertes.fr/hal-01947860},
BOOKTITLE = {{DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning}},
ADDRESS = {Poznan, Poland},
YEAR = {2018},
MONTH = Nov,
HAL_ID = {hal-01947860},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Paper
%F Oral
%T Incremental Learning of Simple Ranking Method Using Reference Profiles Models
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Lab-STICC_UBS_CID_DECIDE
%+ Lab-STICC_UBO_CACS_MOCS
%+ Lab-STICC_UBS_CACS_MOCS
%+ Unité mixte de physique CNRS/Thalès (UMP CNRS/THALES)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%A Khannoussi, Arwa
%A OLTEANU, Alexandru Liviu
%A Dezan, Catherine
%A Diguet, Jean-Philippe
%A Labreuche, Christophe
%A Petit-Frère, Jacques
%A Meyer, Patrick
%< avec comité de lecture
%B DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning
%C Poznan, Poland
%8 2018-11-22
%D 2018
%Z Computer Science [cs]
%Z Computer Science [cs]/Operations Research [cs.RO]Conference papers
%G English
%L hal-01947860
%U https://hal.archives-ouvertes.fr/hal-01947860
%~ UBS
%~ LAB-STICC_UBS
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC_ENIB
%~ LAB-STICC
%~ LAB-STICC_UBO_CACS
%~ LAB-STICC_UBO
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ IBNM
%~ LAB-STICC_IMTA
Communication dans un congrès
Valko Arthur, Olteanu Alexandru Liviu, Brosset David, Meyer Patrick
Integrating a temporal component into multi-criteria majority-rule sorting models
DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning, Nov 2018, Poznan, Poland
Bibtext :
@inproceedings{valko:hal-01947849,
TITLE = {{Integrating a temporal component into multi-criteria majority-rule sorting models}},
AUTHOR = {Valko, Arthur and OLTEANU, Alexandru Liviu and Brosset, David and Meyer, Patrick},
URL = {https://hal.archives-ouvertes.fr/hal-01947849},
BOOKTITLE = {{DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning}},
ADDRESS = {Poznan, Poland},
YEAR = {2018},
MONTH = Nov,
HAL_ID = {hal-01947849},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Paper
%F Oral
%T Integrating a temporal component into multi-criteria majority-rule sorting models
%+ Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
%+ Lab-STICC_UBS_CID_DECIDE
%+ Institut de Recherche de l'Ecole Navale (EA 3634) (IRENAV)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%A Valko, Arthur
%A OLTEANU, Alexandru Liviu
%A Brosset, David
%A Meyer, Patrick
%< avec comité de lecture
%B DA2PL'2018: from Multiple Criteria Decision Aid to Preference Learning
%C Poznan, Poland
%8 2018-11-22
%D 2018
%Z Computer Science [cs]
%Z Computer Science [cs]/Operations Research [cs.RO]Conference papers
%G English
%L hal-01947849
%U https://hal.archives-ouvertes.fr/hal-01947849
%~ UBS
%~ LAB-STICC_UBS
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Chapitre d'ouvrage
Itani Alya, Brisson Laurent, Garlatti Serge
Understanding Learner’s Drop-Out in MOOCs
Yin Hujun; Camacho David; Novais Paulo; Antonio J; Tallón-Ballesteros. Intelligent Data Engineering and Automated Learning -- IDEAL 2018, Springer International Publishing, pp.233-244, 2018, Lecture Notes in Computer Science book series (LNCS, volume 11314), 978-3-030-03493-1. 〈10.1007/978-3-030-03493-1_25〉
Bibtext :
@incollection{itani:hal-01953030,
TITLE = {{Understanding Learner's Drop-Out in MOOCs}},
AUTHOR = {ITANI, Alya and Brisson, Laurent and Garlatti, Serge},
URL = {https://hal.archives-ouvertes.fr/hal-01953030},
BOOKTITLE = {{Intelligent Data Engineering and Automated Learning -- IDEAL 2018}},
EDITOR = {Yin Hujun and Camacho David and Novais Paulo and Antonio J and Tall{\'o}n-Ballesteros},
PUBLISHER = {{Springer International Publishing}},
SERIES = {Lecture Notes in Computer Science book series (LNCS, volume 11314)},
PAGES = {233-244},
YEAR = {2018},
MONTH = Nov,
DOI = {10.1007/978-3-030-03493-1\_25},
KEYWORDS = {Learning Analytics for Learners ; Supervised Machine Learning ; Massive Open Online Courses ; Modeling drop-out},
HAL_ID = {hal-01953030},
HAL_VERSION = {v1},
}
Endnote :
%0 Book Section
%T Understanding Learner’s Drop-Out in MOOCs
%+ Lab-STICC_IMTA_CID_IHSEV
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Lab-STICC_IMTA_CID_DECIDE
%A ITANI, Alya
%A Brisson, Laurent
%A Garlatti, Serge
%@ 978-3-030-03493-1
%B Intelligent Data Engineering and Automated Learning -- IDEAL 2018
%E Yin Hujun
%E Camacho David
%E Novais Paulo
%E Antonio J
%E Tallón-Ballesteros
%I Springer International Publishing
%C Madrid
%S Lecture Notes in Computer Science book series (LNCS, volume 11314)
%P 233-244
%8 2018-11-09
%D 2018
%R 10.1007/978-3-030-03493-1_25
%K Learning Analytics for Learners
%K Supervised Machine Learning
%K Massive Open Online Courses
%K Modeling drop-out
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Statistics [stat]/Machine Learning [stat.ML]Book sections
%X This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this drop-out. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider OpenClassrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners’ engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.
%G English
%L hal-01953030
%U https://hal.archives-ouvertes.fr/hal-01953030
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ LAB-STICC_IMTA_CID_IHSEV
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Communication dans un congrès
Salotti Julien, Fenet Serge, Billot Romain, Faouzi Nour-Eddin, Solnon Christine
Comparison of traffic forecasting methods in urban and suburban context
Internationale Conference on Tools with Artificial Intelligence (ICTAI), Nov 2018, Volos, Greece. IEEE, 2018
Bibtext :
@inproceedings{salotti:hal-01895136,
TITLE = {{Comparison of traffic forecasting methods in urban and suburban context}},
AUTHOR = {Salotti, Julien and Fenet, Serge and Billot, Romain and Faouzi, Nour-Eddin El and Solnon, Christine},
URL = {https://hal.archives-ouvertes.fr/hal-01895136},
BOOKTITLE = {{Internationale Conference on Tools with Artificial Intelligence (ICTAI)}},
ADDRESS = {Volos, Greece},
PUBLISHER = {{IEEE}},
YEAR = {2018},
MONTH = Nov,
KEYWORDS = {ARIMA ; VAR ; k-NN ; Lasso ; SVR ; Variable Selection ; Traffic Forecasting ; Time Series},
PDF = {https://hal.archives-ouvertes.fr/hal-01895136/file/main.pdf},
HAL_ID = {hal-01895136},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T Comparison of traffic forecasting methods in urban and suburban context
%+ Data Mining and Machine Learning (DM2L)
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE)
%+ Geometry Processing and Constrained Optimization (M2DisCo)
%A Salotti, Julien
%A Fenet, Serge
%A Billot, Romain
%A Faouzi, Nour-Eddin, El
%A Solnon, Christine
%< avec comité de lecture
%B Internationale Conference on Tools with Artificial Intelligence (ICTAI)
%C Volos, Greece
%I IEEE
%8 2018-11-05
%D 2018
%K ARIMA
%K VAR
%K k-NN
%K Lasso
%K SVR
%K Variable Selection
%K Traffic Forecasting
%K Time Series
%Z Statistics [stat]/Machine Learning [stat.ML]
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]Conference papers
%X In the context of Connected and Smart Cities, the need to predict short term traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, there is however still no clear view of the requirements involved in network-wide traffic forecasting. In this paper, we study the ability of several state-of-the-art methods to forecast the traffic flow at each road segment. Some of the multivariate methods use the information of all sensors to predict traffic at a specific location, whereas some others rely on the selection of a suitable subset. In addition to classical methods, we also study the advantage of learning this subset by using a new variable selection algorithm based on time series graphical models and information theory. This method has already been successfully used in natural science applications with similar goals, but not in the traffic community. A contribution is to evaluate all these methods on two real-world datasets with different characteristics and to compare the forecasting ability of each method in both contexts. The first dataset describes the traffic flow in the city center of Lyon (France), which exhibits complex patterns due to the network structure and urban traffic dynamics. The second dataset describes inter-urban freeway traffic on the outskirts of the french city of Marseille. Experimental results validate the need for variable selection mechanisms and illustrate the complementarity of forecasting algorithms depending on the type of road and the forecasting horizon.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01895136/document
%2 https://hal.archives-ouvertes.fr/hal-01895136/file/main.pdf
%L hal-01895136
%U https://hal.archives-ouvertes.fr/hal-01895136
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ EC-LYON
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ IFSTTAR
%~ UNIV-LYON1
%~ INSA-LYON
%~ ENTPE
%~ LIRIS
%~ LAB-STICC_IMTA_CID_DECIDE
%~ LYON2
%~ UNIV-LYON2
%~ INSA-GROUPE
%~ IMT-ATLANTIQUE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Communication dans un congrès
Jacq Olivier, Boudvin Xavier, Brosset David, Kermarrec Yvon, Simonin Jacques
Detecting and Hunting Cyberthreats in a Maritime Environment: Specification and Experimentation of a Maritime Cybersecurity Operations Centre
Cyber Security In Networking Conference, Oct 2018, Paris, France. Proceedings Cyber Security In Networking Conference, 2018
Bibtext :
@inproceedings{jacq:hal-01911640,
TITLE = {{Detecting and Hunting Cyberthreats in a Maritime Environment: Specification and Experimentation of a Maritime Cybersecurity Operations Centre}},
AUTHOR = {Jacq, Olivier and Boudvin, Xavier and Brosset, David and Kermarrec, Yvon and Simonin, Jacques},
URL = {https://hal.archives-ouvertes.fr/hal-01911640},
BOOKTITLE = {{Cyber Security In Networking Conference}},
ADDRESS = {Paris, France},
HAL_LOCAL_REFERENCE = {19267},
PAGES = {.},
YEAR = {2018},
MONTH = Oct,
KEYWORDS = {ICS ; SOC ; Maritime ; Cyber situation awareness},
HAL_ID = {hal-01911640},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T Detecting and Hunting Cyberthreats in a Maritime Environment: Specification and Experimentation of a Maritime Cybersecurity Operations Centre
%+ Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Département Systèmes Réseaux, Cybersécurité et Droit du numérique (SRCD)
%+ Lab-STICC_IMTA_CID_IRIS
%+ Lab-STICC_IMTA_CID_DECIDE
%A Jacq, Olivier
%A Boudvin, Xavier
%A Brosset, David
%A Kermarrec, Yvon
%A Simonin, Jacques
%< avec comité de lecture
%Z 19267
%( Proceedings Cyber Security In Networking Conference
%B Cyber Security In Networking Conference
%C Paris, France
%P .
%8 2018-10-24
%D 2018
%K ICS
%K SOC
%K Maritime
%K Cyber situation awareness
%Z Computer Science [cs]/Computers and Society [cs.CY]Conference papers
%X The vast majority of worldwide goods exchanges are made by sea. In some parts of the world, the concurrence for dominance at sea is very high and definitely seen as a main military goal. Meanwhile, new generation ships highly rely on information systems for communication, navigation and platform management. This ever-spreading attack surface and permanent satellite links have grown a concern about the potential impact of cyberattacks on a ship at sea or on naval shore infrastructures. Therefore, on top of the usual cyberprotection measures taken for safety reasons, it is essential to implement an ongoing cyber monitoring of ships in order to detect, react accordingly, and stop any incoming threat. In this paper, we explain the specific constraints when trying to assess the cyber situation awareness of maritime information systems. As we will demonstrate, those systems combine physical and logical constraints which complexify their cyber monitoring process and architecture. Gathering valuable data while having a limited and controlled impact on the satellite bandwidth, maintaining a high level of integrity on remote systems in production are, for instance, thriving challenges for both civilian and military ships. We have designed and set up a research platform which fulfils those specifications to streamline the cyber monitoring process.We will then describe the architecture used to detect cyber-threats and collect potential Indices of Compromise from naval systems, as well as the results we have currently achieved.
%G English
%L hal-01911640
%U https://hal.archives-ouvertes.fr/hal-01911640
%~ CNRS
%~ UNIV-UBS
%~ UNIV-BREST
%~ INSTITUT-TELECOM
%~ ENIB
%~ TDS-MACS
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ IMTA_SRCD
%~ LAB-STICC_IMTA_CID_IRIS
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
Communication dans un congrès
Simonnet Mathieu, Morvan Serge, Marques Dominique, Ducruix Olivier, Grancher Arnaud, Kerouedan Sylvie
Maritime Buoyage on 3D-Printed Tactile Maps
the 20th International ACM SIGACCESS Conference, Oct 2018, Galway, France. ACM Press, 〈10.1145/3234695.3241007〉
Bibtext :
@inproceedings{simonnet:hal-01907640,
TITLE = {{Maritime Buoyage on 3D-Printed Tactile Maps}},
AUTHOR = {Simonnet, Mathieu and Morvan, Serge and Marques, Dominique and Ducruix, Olivier and grancher, arnaud and KEROUEDAN, Sylvie},
URL = {https://hal-imt-atlantique.archives-ouvertes.fr/hal-01907640},
BOOKTITLE = {{the 20th International ACM SIGACCESS Conference}},
ADDRESS = {Galway, France},
PUBLISHER = {{ACM Press}},
YEAR = {2018},
MONTH = Oct,
DOI = {10.1145/3234695.3241007},
HAL_ID = {hal-01907640},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Paper
%F Oral
%T Maritime Buoyage on 3D-Printed Tactile Maps
%+ Laboratoire d'Economie et de Gestion de l'Ouest (LEGO)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Centre Européen de Réalité Virtuelle (CERV)
%+ Lab-STICC_TB_CACS_IAS
%A Simonnet, Mathieu
%A Morvan, Serge
%A Marques, Dominique
%A Ducruix, Olivier
%A grancher, arnaud
%A KEROUEDAN, Sylvie
%< avec comité de lecture
%B the 20th International ACM SIGACCESS Conference
%C Galway, France
%I ACM Press
%8 2018-10-22
%D 2018
%R 10.1145/3234695.3241007
%Z Cognitive science/Psychology
%Z Cognitive science/Computer scienceConference papers
%G English
%L hal-01907640
%U https://hal-imt-atlantique.archives-ouvertes.fr/hal-01907640
%~ IMT-ATLANTIQUE
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ CERV
%~ LAB-STICC
%~ LAB-STICC_TB
%~ LEGO-IMTA
%~ LEGO
%~ LAB-STICC_IMTA
%~ LEGO_IMTA
%~ LAB-STICC_IMTA_CACS_IAS
%~ IMTA_LUSSI
%~ IBSHS
Communication dans un congrès
Kabil Alexandre, Duval Thierry, Cuppens Nora, Le Comte Gérard, Halgand Yoran, Ponchel Christophe
3D CyberCOP: a Collaborative Platform for Cybersecurity Data Analysis and Training
Luo, Yuhua. 15th International Conference on Cooperative Design, Visualization and Engineering, Oct 2018, Hangzou, China. Springer, pp.176-183, Cooperative Design, Visualization, and Engineering. 〈http://www.cdve.org/〉
Bibtext :
@inproceedings{kabil:hal-01831965,
TITLE = {{3D CyberCOP: a Collaborative Platform for Cybersecurity Data Analysis and Training}},
AUTHOR = {Kabil, Alexandre and Duval, Thierry and Cuppens, Nora and Le Comte, G{\'e}rard and Halgand, Yoran and Ponchel, Christophe},
URL = {https://hal.archives-ouvertes.fr/hal-01831965},
NOTE = {in proceedings of CDVE 2018 (15th International Conference on Cooperative Design, Visualization and Engineering), Springer, p. 176-183, Hangzhou, China, October 21-24, 2018},
BOOKTITLE = {{15th International Conference on Cooperative Design, Visualization and Engineering}},
ADDRESS = {Hangzou, China},
EDITOR = {Luo, Yuhua},
PUBLISHER = {{Springer}},
SERIES = {Cooperative Design, Visualization, and Engineering},
PAGES = {176-183},
YEAR = {2018},
MONTH = Oct,
PDF = {https://hal.archives-ouvertes.fr/hal-01831965/file/CDVE2018.pdf},
HAL_ID = {hal-01831965},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T 3D CyberCOP: a Collaborative Platform for Cybersecurity Data Analysis and Training
%+ Lab-STICC_IMTA_CID_IHSEV
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Lab-STICC_IMTA_CID_IRIS
%+ Département Systèmes Réseaux, Cybersécurité et Droit du numérique (SRCD)
%+ Société Générale
%+ EDF (EDF)
%+ Airbus Defence & Space [Elancourt] (Airbus group)
%A Kabil, Alexandre
%A Duval, Thierry
%A Cuppens, Nora
%A Le Comte, Gérard
%A Halgand, Yoran
%A Ponchel, Christophe
%Z in proceedings of CDVE 2018 (15th International Conference on Cooperative Design, Visualization and Engineering), Springer, p. 176-183, Hangzhou, China, October 21-24, 2018
%< avec comité de lecture
%B 15th International Conference on Cooperative Design, Visualization and Engineering
%C Hangzou, China
%Y Luo, Yuhua
%I Springer
%3 Cooperative Design, Visualization, and Engineering
%P 176-183
%8 2018-10-21
%D 2018
%Z Computer Science [cs]/Graphics [cs.GR]
%Z Computer Science [cs]/Human-Computer Interaction [cs.HC]
%Z Computer Science [cs]/Cryptography and Security [cs.CR]Conference papers
%X Although Immersive Analytics solutions are now developed in order to ease data analysis, cyber security systems are still using classical graphical representations and are not harnessing yet the potential of virtual reality systems and collaborative virtual environments. 3D Col-laborative Virtual Environments (3DCVE) can be used in order to merge learning and data analysis approaches, as they can allow users to have a better understanding of a cyber situation by mediating interactions towards them and also by providing different points of view of the same data, on different scales. So we propose a 3D Cyber Common Operational Picture (3D CyberCOP) that will allow operators to face together a situation by using immersive and non immersive visualizations and by collaborating through user-defined roles. After visiting French Security Operations Centers (SOCs), we have defined a collaborative interaction model and some use-cases, to assess of the effectiveness of our solution.
%G English
%Z Chaire Cyber CNI
%2 https://hal.archives-ouvertes.fr/hal-01831965/document
%2 https://hal.archives-ouvertes.fr/hal-01831965/file/CDVE2018.pdf
%L hal-01831965
%U https://hal.archives-ouvertes.fr/hal-01831965
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ IMTA_SRCD
%~ LAB-STICC_IMTA_CID_IHSEV
%~ UNIV-BREST
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_IRIS
%~ IMTA_LUSSI
%~ EDF
%~ LAB-STICC_IMTA
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