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342 results on '"Owezarski, Philippe"'

1. A Controversial Study on Random Forest Accuracy for Attack Detection

Author

Vacher, Quentin, Owezarski, Philippe, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2023
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2. Design of a 5G Experimental Platform Based on OpenAirInterface

Author

Douarre, Quentin, Djelloul, El-Mehdi, Berthou, Pascal, Dragomirescu, Daniela, Owezarski, Philippe, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Yu, Shui, editor, Gu, Bruce, editor, Qu, Youyang, editor, and Wang, Xiaodong, editor

Published
2023
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5. An SDN hybrid architecture for vehicular networks: Application to Intelligent Transport System

Author

Toufga, Soufian, Owezarski, Philippe, Abdellatif, Slim, and Villemur, Thierry

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Vehicular networks are one of the cornerstone of an Intelligent Transportation System (ITS). They are expected to provide ubiquitous network connectivity to moving vehicles while supporting various ITS services, some with very stringent requirements in terms of latency and reliability. Two vehicular networking technologies are envisioned to jointly support the full range of ITS services : DSRC (Dedicated Short Range Communication) for direct vehicle to vehicle/Road Side Units (RSU) communications and cellular technologies. To the best of our knowledge, approaches from the literature usually divide ITS services on each of these networks according to their requirements and one single network is in charge of supporting the each service. Those that consider both network technologies to offer multi-path routing, load balancing or path splitting for a better quality of experience of ITS services assume obviously separately controlled networks. Under the umbrella of SDN (Software Defined Networking), we propose in this paper a hybrid network architecture that enables the joint control of the networks providing connectivity to multi-homed vehicles and, also, explore the opportunities brought by such an architecture. We show through some use cases, that in addition to the flexibility and fine-grained programmability brought by SDN, it opens the way towards the development of effective network control algorithms that are the key towards the successful support of ITS services and especially those with stringent QoS. We also show how these algorithms could also benefit from information related to the environment or context in which vehicles evolve (traffic density, planned trajectory, ..), which could be easily collected by data providers and made available via the cloud.

Published
2017

6. Study and Evaluation of Unsupervised Algorithms Used in Network Anomaly Detection

Author

Dromard, Juliette, Owezarski, Philippe, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Bhatia, Rahul, editor, and Kapoor, Supriya, editor

Published
2020
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11. Investigating adversarial attacks against Random Forest-based network attack detection systems

Author

Owezarski, Philippe, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), IEEE/IFIP, and IEEE

Subjects
Machine Learning, Random Forest, Network Attacks Detection, Adversarial learning, [INFO]Computer Science [cs]
Abstract

International audience; A significant research effort in cybersecurity currently deals with Machine Learning-based attack detection. It is aimed at providing autonomous attack detection systems that require less human expert resources, and are then less expensive in time and money. Indeed, such systems are able to autonomously learn about benign and malicious traffic, and to classify further traffic samples accordingly. In such context, hackers start designing adversarial learning approaches in order to design new attacks able to evade from the Machine Learningbased detection systems. The work presented in this paper aims at exhibiting how easy it is to modify existing attacks to make them evade from the Machine Learning-based attack detectors. The Random Forest algorithm has been selected for this work as it is globally evaluated as one of the best Machine Learning algorithm for cybersecurity, and it provides informations on how a decision is made. Indeed, the analysis of the related Random Forest trees helps explaining the limits of this Machine Learning algorithm, and gives some information that could be helpful for making attack detection somewhat explainable. Several other Machine Learning algorithms as SVM, kNN ans LSTM have been selected for evaluating their ability to detect the adversarial attack presented in this paper.

Published
2023

12. Predictive Estimation of Wireless Link Performance from Medium Physical Parameters Using Support Vector Regression and k-Nearest Neighbors

Author

Kremer, Guillaume, Owezarski, Philippe, Berthou, Pascal, Capdehourat, German, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Dainotti, Alberto, editor, Mahanti, Anirban, editor, and Uhlig, Steve, editor

Published
2014
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13. Risk Assessment for Airworthiness Security

Author

Gil Casals, Silvia, Owezarski, Philippe, Descargues, Gilles, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ortmeier, Frank, editor, and Daniel, Peter, editor

Published
2012
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14. First Step in Cross-Layers Measurement in Wireless Networks How to Adapt to Resource Constraints for Optimizing End-to-End Services?

Author

Owezarski, Philippe, Hasan, Rasha Ghassan, Kremer, Guillaume, Berthou, Pascal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Masip-Bruin, Xavier, editor, Verchere, Dominique, editor, Tsaoussidis, Vassilis, editor, and Yannuzzi, Marcelo, editor

Published
2011
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15. oBGP: An Overlay for a Scalable iBGP Control Plane

Author

Oprescu, Iuniana, Meulle, Mickaël, Uhlig, Steve, Pelsser, Cristel, Maennel, Olaf, Owezarski, Philippe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Domingo-Pascual, Jordi, editor, Manzoni, Pietro, editor, Palazzo, Sergio, editor, Pont, Ana, editor, and Scoglio, Caterina, editor

Published
2011
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16. UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking

Author

Casas, Pedro, Mazel, Johan, Owezarski, Philippe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Domingo-Pascual, Jordi, editor, Manzoni, Pietro, editor, Palazzo, Sergio, editor, Pont, Ana, editor, and Scoglio, Caterina, editor

Published
2011
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17. Sub-Space Clustering and Evidence Accumulation for Unsupervised Network Anomaly Detection

Author

Mazel, Johan, Casas, Pedro, Owezarski, Philippe, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Domingo-Pascual, Jordi, editor, Shavitt, Yuval, editor, and Uhlig, Steve, editor

Published
2011
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19. 0day Anomaly Detection Made Possible Thanks to Machine Learning

Author

Owezarski, Philippe, Mazel, Johan, Labit, Yann, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Osipov, Evgeny, editor, Kassler, Andreas, editor, Bohnert, Thomas Michael, editor, and Masip-Bruin, Xavier, editor

Published
2010
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20. A Database of Anomalous Traffic for Assessing Profile Based IDS

Author

Owezarski, Philippe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ricciato, Fabio, editor, Mellia, Marco, editor, and Biersack, Ernst, editor

Published
2010
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21. Automated Classification of Network Traffic Anomalies

Author

Fernandes, Guilherme, Owezarski, Philippe, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Chen, Yan, editor, Dimitriou, Tassos D., editor, and Zhou, Jianying, editor

Published
2009
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22. Design of a 5G experimental platform based on OpenAirInterface

Author

Douarre, Quentin, Djelloul, El-Mehdi, Berthou, Pascal, Dragomirescu, Daniela, Owezarski, Philippe, Owezarski, Philippe, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Équipe MIcro et Nanosystèmes pour les Communications sans fil (LAAS-MINC)

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], network softwarization, Experimental platform, OpenAirInterface, 5G
Abstract

International audience; 5G has been designed for providing the appropriate services for a large range of applications requiring high throughput, low latency, a support for the IoT, or for Industry 4.0 business, etc. One of its strong statements is the sofwarization of most of its functions for providing more flexibility, and a support that can easily evolve for providing new services. In that context, OpenSource implementations of 5G functions arise. One of this implementation is the 5G OpenAirInterface (OAI). This paper then describes how a 5G experimental platform taking advantage of the 5G OAI software suite was designed and deployed at LAAS-CNRS. The aim of the platform is to be as generic as possible for being able to experiment and evaluate all 5G new mechanisms and protocols issued from researchers. This paper then specifically addresses how the compatibility issues between 5G OAI and the equipments (USRP, servers, operating systems, etc.) were fixed. The paper also proposes a performance evaluation of the 5G OAI platform and analyzes its limits.

Published
2022

24. NADA – Network Anomaly Detection Algorithm

Author

Farraposo, Sílvia, Owezarski, Philippe, Monteiro, Edmundo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Clemm, Alexander, editor, Granville, Lisandro Zambenedetti, editor, and Stadler, Rolf, editor

Published
2007
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25. Internet Traffic Characterization – An Analysis of Traffic Oscillations

Author

Owezarski, Philippe, Larrieu, Nicolas, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mammeri, Zoubir, editor, and Lorenz, Pascal, editor

Published
2004
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31. An explainable-by-design ensemble learning system to detect unknown network attacks

Author

Minh, Céline, Vermeulen, Kevin, Lefebvre, Cédric, Owezarski, Philippe, Ritchie, William, Minh, Céline, Custocy, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), LAAS - CNRS, Cyblex Technologies, and LAAS-CNRS

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

Machine learning (ML) is a promising technology for network intrusion detection systems. There is a wide range of ML algorithms that are potential candidates for network intrusion detection systems, as they exhibit very good detection accuracy in average. However, significant detection differences appear when facing different kinds of attacks, some being prone to better detect some particular attack types. They then often appear to complement each other. The challenge then lies in determining the accurate result when several ML models provide different results, and this without any explanation about their decision. To address this challenge, our system aims to reconstruct attack patterns from the outputs of these ML models and presenting them in an interpretable manner. For that, we propose an approach combining ensemble learning and stacking with a meta-learner that works on graphical representation of traffic flows, that then provides the required explainability level for the decisions made. The evaluation of our system, using the CSE-CIC-IDS2018 dataset, demonstrates a significant improvement achieved through the combination of multiple ML algorithms. Furthermore, we emphasize the importance of explainability in network intrusion detection systems and the need for accurate and interpretable models. Our system goes beyond traditional detection methods by reporting anomalous feature pairs and providing visual representations of attack patterns, empowering analysts to better understand and respond to network threats.

Published
2022

34. Using Distributed Tracing to Identify Inefficient Resources Composition in Cloud Applications

Author

Cassé, Clément, Berthou, Pascal, Owezarski, Philippe, Josset, Sébastien, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Orange Labs, and Orange Labs [Meylan]

Subjects
Distributed Tracing, Hierarchical Model, Property Graph, Cloud Computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Graph Rewriting
Abstract

International audience; Cloud-Applications are the new industry standard way of designing Web-Applications. With Cloud Computing, Applications are usually designed as microservices, and developers can take advantage of thousands of such existing microservices, involving several hundred of cross-component communications on different physical resources. Microservices orchestration (as Kubernetes) is an automatic process, which manages each component lifecycle, and notably their allocation on the different resources of the cloud infrastructure. Whereas such automatic cloud technologies ease development and deployment, they nevertheless obscure debugging and performance analysis. In order to gain insight on the composition of services, distributed tracing recently emerged as a way to get the decomposition of the activity of each component within a cloud infrastructure. This paper aims at providing methodologies and tools (leveraging state-of-the-art tracing) for getting a wider view of application behaviours, especially focusing on application performance assessment. In this paper, we focus on using distributed traces and allocation information from microservices to model their dependencies as a hierarchical property graph. By applying graph rewriting operations, we managed to project and filter communications observed between microservices at higher abstraction layers like the machine nodes, the zones or regions. Finally, in this paper we propose an implementation of the model running on a microservices shopping application deployed on a Zonal Kubernetes cluster monitored by OpenTelemetry traces. We propose using the flow hierarchy metric on the graph model to pinpoint cycles that reveal inefficient resource composition inducing possible performance issues and economic waste.

Published
2021

40. Une caractérisation non gaussienne et à longue mémoire du trafic Internet et de ses anomalies: validation expérimentale et application à la détection d’attaque de DDoS/Non Gaussian Long Memory Model for Internet Traffic: Experimental Validation and Application to DDoS Detection

Author

Borgnat, Pierre, Abry, Patrice, Dewaele, Guillaume, Scherrer, Antoine, Larrieu, Nicolas, Owezarski, Philippe, Labit, Yann, Gallon, Laurent, and Aussibal, Julien

Published
2007
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44. Ontology Based Anomaly Detection for Cellular Vehicular Communications

Author

Ricard, Quentin, Owezarski, Philippe, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], intelligent transportation systems, C-V2X, anomaly detection
Abstract

International audience; Intelligent Transportation Systems are being deployed all over the world, providing new applications and services that could prevent accidents, help regulate traffic and the automotive industry in designing energy efficient vehicles. However, enabling vehicles to communicate with the rest of the world ultimately leads to new security challenges with connected vehicles becoming new interesting targets for malicious actors. Thus, safeguards need to be deployed to detect malicious and anomalous activities in vehicular communications. This paper presents an approach to anomaly detection based on an ontological representation of cellular vehicular communication.

Published
2020

50. Modeling Internet backbone traffic at the flow level

Author

Barakat, Chadi, Thiran, Patrick, Iannaccone, Gianluca, Diot, Christophe, and Owezarski, Philippe

Subjects
Backbone networks -- Research, Internet -- Research, Signal processing -- Research, Backbone network, Internet, Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The aim is to design a traffic model for Internet backbone links. The model should be simple while being as general as possible.

Published
2003

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