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Your search keyword '"Muhlethaler, Paul"' showing total 109 results
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109 results on '"Muhlethaler, Paul"'

4. Attacks, Detection Mechanisms and Their Limits in Named Data Networking (NDN)

Author

Hidouri, Abdelhak, Hadded, Mohamed, Touati, Haifa, Hajlaoui, Nasreddine, Muhlethaler, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Hendrix, Eligius M. T., editor, Taniar, David, editor, and Apduhan, Bernady O., editor

Published
2022
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5. A Secure Data Storage in Multi-cloud Architecture Using Blowfish Encryption Algorithm

Author

Ghanmi, Houaida, Hajlaoui, Nasreddine, Touati, Haifa, Hadded, Mohamed, Muhlethaler, Paul, 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, Barolli, Leonard, editor, Hussain, Farookh, editor, and Enokido, Tomoya, editor

Published
2022
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6. A Detection Mechanism for Cache Pollution Attack in Named Data Network Architecture

Author

Hidouri, Abdelhak, Touati, Haifa, Hadded, Mohamed, Hajlaoui, Nasreddine, Muhlethaler, Paul, 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, Barolli, Leonard, editor, Hussain, Farookh, editor, and Enokido, Tomoya, editor

Published
2022
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7. An Efficient Cross-Layer Design for Multi-hop Broadcast of Emergency Warning Messages in Vehicular Networks

Author

Rebei, Abir, Boukhalfa, Fouzi, Touati, Haifa, Hadded, Mohamed, Muhlethaler, Paul, 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, Barolli, Leonard, editor, Woungang, Isaac, editor, and Enokido, Tomoya, editor

Published
2021
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10. An empirical study of Unfairness and Oscillation in ETSI DCC

Author

Marzouk, Fatma, Zagrouba, Rachid, Laouiti, Anis, and Muhlethaler, Paul

Subjects
Computer Science - Networking and Internet Architecture
Abstract

-Performance of Vehicular Adhoc Networks (VANETs) in high node density situation has long been a major field of studies. Particular attention has been paid to the frequent exchange of Cooperative Awareness Messages (CAMs) on which many road safety applications rely. In the present paper, se focus on the European Telecommunications Standard Institute (ETSI) Decentralized Congestion Control (DCC) mechanism, particularly on the evaluation of its facility layers component when applied in the context of dense networks. For this purpose, a set of simulations has been conducted over several scenarios, considering rural highway and urban mobility in order to investigate unfairness and oscillation issues, and analyze the triggering factors. The experimental results show that the latest technical specification of the ETSI DCC presents a significant enhancement in terms of fairness. In contrast, the stability criterion leaves room for improvement as channel load measurement presents (i) considerable fluctuations when only the facility layer control is applied and (i.i) severe state oscillation when different DCC control methods are combined.

Published
2015

11. A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with Buffering

Author

Fayolle, Guy and Muhlethaler, Paul

Subjects
Computer Science - Performance, Mathematics - Probability, Primary 60J10, secondary 30D05, 30E99
Abstract

The purpose of this paper is to analyze the so-called back-off technique of the IEEE 802.11 protocol in broadcast mode with waiting queues. In contrast to existing models, packets arriving when a station (or node) is in back-off state are not discarded, but are stored in a buffer of infinite capacity. As in previous studies, the key point of our analysis hinges on the assumption that the time on the channel is viewed as a random succession of transmission slots (whose duration corresponds to the length of a packet) and mini-slots during which the back-o? of the station is decremented. These events occur independently, with given probabilities. The state of a node is represented by a two-dimensional Markov chain in discrete-time, formed by the back-off counter and the number of packets at the station. Two models are proposed both of which are shown to cope reasonably well with the physical principles of the protocol. The stabillity (ergodicity) conditions are obtained and interpreted in terms of maximum throughput. Several approximations related to these models are also discussed.

Published
2015
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15. An Active Signaling Mechanism to Reduce Access Collisions in a Distributed TDMA Based MAC Protocol for Vehicular Networks

Author

Boukhalfa, Fouzi, Hadded, Mohamed, Muhlethaler, Paul, Shagdar, Oyunchimeg, 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, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2020
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18. Position Certainty Propagation: A Location Service for MANETs

Author

Sobehy, Abdallah, Renault, Eric, Muhlethaler, Paul, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Renault, Éric, editor, Boumerdassi, Selma, editor, and Bouzefrane, Samia, editor

Published
2019
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23. A Novel Angle-Based Clustering Algorithm for Vehicular Ad Hoc Networks

Author

Hadded, Mohamed, Muhlethaler, Paul, Laouiti, Anis, Azzouz Saidane, Leila, 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, Laouiti, Anis, editor, Qayyum, Amir, editor, and Mohamad Saad, Mohamad Naufal, editor

Published
2017
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28. Automated Header Compression in Constrained Networks

Author

Banerjee, Soumya, Barthel, Dominique, Lampin, Quentin, Dumay, Marion, Coutant, Stephane, Adjih, Cedric, Muhlethaler, Paul, and Watteyne, Thomas

Abstract

In low-power wireless networks, every byte sent by an embedded device causes its radio to stay on a little longer, which eats into its limited energy reserve. And because the radio is often the most power-hungry circuit in the device, reducing the number of bytes to be sent and received automatically increases the battery lifetime of the device, resulting in a lower total cost of ownership for the end-user, hence better adoption. Low-power wireless devices tend to generate short data payload, typically in the order of 2–50 B. This means that protocol headers make up a large portion of the bytes inside a wireless frame, 3070% is not uncommon. Compressing those headers, i.e., removing bytes that can be reconstructed anyways or that are not needed, constrained device constrained network (headers are compressed) compression LBR decompression computer traditional Internet (headers are NOT compressed) makes perfect sense. This article serves as a primer on header compression in constrained networks. We start by describing exactly why it is needed, then survey the different standards doing header compression. We indicate how today's approach requires expert input for every deployment, severely hindering the rollout of such approaches. Instead, we argue that an automated approach based on machine learning and artificial intelligence is the right way to go, and provide blueprints for such approaches.

Published
2024
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33. Machine Learning for Networking - Third International Conference, MLN 2020, Paris, France, November 24-26, 2020, Revised Selected Papers

Author

Renault, Eric, Boumerdassi, Selma, Muhlethaler, Paul, Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, CEDRIC. Réseaux et Objets Connectés (CEDRIC - ROC), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Wireless Networking for Evolving & Adaptive Applications (EVA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), HESAM Université (HESAM)-HESAM Université (HESAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université (HESAM)-HESAM Université (HESAM), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and Institut National de Recherche en Informatique et en Automatique (Inria)

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

International audience

Published
2021
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35. Developing Customized & Secure Blockchains with Deep Federation Learning to Prevent Successive Attacks

Author

Banerjee, Soumya, Chakraborty, Soham, Muhlethaler, Paul, Trasna Solutions, Kalinga Institute of Industrial Technology [Bhubaneswar, Inde] (KIIT), Wireless Networking for Evolving & Adaptive Applications (EVA), Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Blockchain, [INFO]Computer Science [cs], Anomaly detection, Intelligent Algorithm, Autoencoders, Sequence-to-sequence models, Federated Learning
Abstract

International audience; Recently, blockchain technology has been one of the most promising fields of research aiming to enhance the security and privacy of systems. It follows a distributed mechanism to make the storage system fault-tolerant. However, even after adopting all the security measures, there are some risks for cyberattacks in the blockchain. From a statistical point of view, attacks can be compared to anomalous transactions compared to normal transactions. In this paper, these anomalous transactions can be detected using machine learning algorithms, thus making the framework much more secure. Several machine learning algorithms can detect anomalous observations. Due to the typical nature of the transactions dataset (time-series), we choose to apply a sequence to the sequence model. In this paper, we present our approach, where we use federated learning embedded with an LSTM-based autoencoder to detect anomalous transactions.

Published
2020

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