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583 results on '"Danger, Jean-Luc"'

1. Impact of Process Mismatch and Device Aging on SR-Latch Based True Random Number Generators

Author

Bahrami, Javad, Ebrahimabadi, Mohammad, Guilley, Sylvain, Danger, Jean-Luc, Karimi, Naghmeh, 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, Wacquez, Romain, editor, and Homma, Naofumi, editor

Published
2024
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2. High-Order Collision Attack Vulnerabilities in Montgomery Ladder Implementations of RSA

Author

Varillon, Arnaud, Sauvage, Laurent, Danger, Jean-Luc, 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, Regazzoni, Francesco, editor, Mazumdar, Bodhisatwa, editor, and Parameswaran, Sri, editor

Published
2024
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5. CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model

Author

Alkhatib, Natasha, Mushtaq, Maria, Ghauch, Hadi, and Danger, Jean-Luc

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Controller Area Network (CAN), known as the most widely used in-vehicle networking technology, lacks encryption and authentication mechanisms, making the communications delivered by distributed ECUs insecure. Inspired by the outstanding performance of bidirectional encoder representations from transformers (BERT) for improving many natural language processing tasks, we propose in this paper ``CAN-BERT", a deep learning based network intrusion detection system, to detect cyber attacks on CAN bus protocol. We show that the BERT model can learn the sequence of arbitration identifiers (IDs) in the CAN bus for anomaly detection using the ``masked language model" unsupervised training objective. The experimental results on the ``Car Hacking: Attack \& Defense Challenge 2020" dataset show that ``CAN-BERT" outperforms state-of-the-art approaches. In addition to being able to identify in-vehicle intrusions in real-time within 0.8 ms to 3 ms w.r.t CAN ID sequence length, it can also detect a wide variety of cyberattacks with an F1-score of between 0.81 and 0.99.

Published
2022

6. Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

Author

Alkhatib, Natasha, Mushtaq, Maria, Ghauch, Hadi, and Danger, Jean-Luc

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset", show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.

Published
2022

7. SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks

Author

Alkhatib, Natasha, Ghauch, Hadi, and Danger, Jean-Luc

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset with several classes representing realistic intrusions, and a normal class - a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting in-vehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type.

Published
2021

9. Reliability of Ring Oscillator PUFs with Reduced Helper Data

Author

Béguinot, Julien, Cheng, Wei, Danger, Jean-Luc, Guilley, Sylvain, Rioul, Olivier, Yli-Mäyry, Ville, 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, Shikata, Junji, editor, and Kuzuno, Hiroki, editor

Published
2023
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12. Parasite: Mitigating Physical Side-Channel Attacks Against Neural Networks

Author

Chabanne, Hervé, Danger, Jean-Luc, Guiga, Linda, Kühne, Ulrich, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Batina, Lejla, editor, Picek, Stjepan, editor, and Mondal, Mainack, editor

Published
2022
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18. Analysis and Protection of the Two-Metric Helper Data Scheme

Author

Tebelmann, Lars, Kühne, Ulrich, Danger, Jean-Luc, Pehl, Michael, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhasin, Shivam, editor, and De Santis, Fabrizio, editor

Published
2021
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19. Enhancing the Resiliency of Multi-bit Parallel Arbiter-PUF and Its Derivatives Against Power Attacks

Author

Kroeger, Trevor, Cheng, Wei, Guilley, Sylvain, Danger, Jean-Luc, Karimi, Naghmeh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhasin, Shivam, editor, and De Santis, Fabrizio, editor

Published
2021
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20. Highly Reliable PUFs for Embedded Systems, Protected Against Tampering

Author

Danger, Jean-Luc, Guilley, Sylvain, Pehl, Michael, Senni, Sophiane, Souissi, Youssef, 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 (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Vo, Nguyen-Son, editor, Hoang, Van-Phuc, editor, and Vien, Quoc-Tuan, editor

Published
2021
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21. Telepathic Headache: Mitigating Cache Side-Channel Attacks on Convolutional Neural Networks

Author

Chabanne, Hervé, Danger, Jean-Luc, Guiga, Linda, Kühne, Ulrich, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sako, Kazue, editor, and Tippenhauer, Nils Ole, editor

Published
2021
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22. Self-secured PUF: Protecting the Loop PUF by Masking

Author

Tebelmann, Lars, Danger, Jean-Luc, Pehl, Michael, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bertoni, Guido Marco, editor, and Regazzoni, Francesco, editor

Published
2021
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23. Processor Anchor to Increase the Robustness Against Fault Injection and Cyber Attacks

Author

Danger, Jean-Luc, Facon, Adrien, Guilley, Sylvain, Heydemann, Karine, Kühne, Ulrich, Merabet, Abdelmalek Si, Timbert, Michaël, Pecatte, Baptiste, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bertoni, Guido Marco, editor, and Regazzoni, Francesco, editor

Published
2021
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29. High Precision Fault Injections on the Instruction Cache of ARMv7-M Architectures

Author

Rivière, Lionel, Najm, Zakaria, Rauzy, Pablo, Danger, Jean-Luc, Bringer, Julien, and Sauvage, Laurent

Subjects
Computer Science - Cryptography and Security
Abstract

Hardware and software of secured embedded systems are prone to physical attacks. In particular, fault injection attacks revealed vulnerabilities on the data and the control flow allowing an attacker to break cryptographic or secured algorithms implementations. While many research studies concentrated on successful attacks on the data flow, only a few targets the instruction flow. In this paper, we focus on electromagnetic fault injection (EMFI) on the control flow, especially on the instruction cache. We target the very widespread (smartphones, tablets, settop-boxes, health-industry monitors and sensors, etc.) ARMv7-M architecture. We describe a practical EMFI platform and present a methodology providing high control level and high reproducibility over fault injections. Indeed, we observe that a precise fault model occurs in up to 96% of the cases. We then characterize and exhibit this practical fault model on the cache that is not yet considered in the literature. We comprehensively describe its effects and show how it can be used to reproduce well known fault attacks. Finally, we describe how it can benefits attackers to mount new powerful attacks or simplify existing ones., Comment: HOST 2015: IEEE International Symposium on Hardware-Oriented Security and Trust, May 2015, Washington, United States

Published
2015
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30. Security Evaluation Against Side-Channel Analysis at Compilation Time

Author

Bruneau, Nicolas, Christen, Charles, Danger, Jean-Luc, Facon, Adrien, Guilley, Sylvain, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gueye, Cheikh Thiecoumba, editor, Persichetti, Edoardo, editor, Cayrel, Pierre-Louis, editor, and Buchmann, Johannes, editor

Published
2019
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31. Prediction-Based Intrusion Detection System for In-Vehicle Networks Using Supervised Learning and Outlier-Detection

Author

Karray, Khaled, Danger, Jean-Luc, Guilley, Sylvain, Elaabid, Moulay Abdelaziz, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Blazy, Olivier, editor, and Yeun, Chan Yeob, editor

Published
2019
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32. Generic Architecture for Lightweight Block Ciphers: A First Step Towards Agile Implementation of Multiple Ciphers

Author

Tehrani, Etienne, Danger, Jean-Luc, Graba, Tarik, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Blazy, Olivier, editor, and Yeun, Chan Yeob, editor

Published
2019
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33. Development of the Unified Security Requirements of PUFs During the Standardization Process

Author

Bruneau, Nicolas, Danger, Jean-Luc, Facon, Adrien, Guilley, Sylvain, Hamaguchi, Soshi, Hori, Yohei, Kang, Yousung, Schaub, Alexander, 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, Lanet, Jean-Louis, editor, and Toma, Cristian, editor

Published
2019
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36. Multiply Constant-Weight Codes and the Reliability of Loop Physically Unclonable Functions

Author

Chee, Yeow Meng, Cherif, Zouha, Danger, Jean-Luc, Guilley, Sylvain, Kiah, Han Mao, Kim, Jon-Lark, Solé, Patrick, and Zhang, Xiande

Subjects
Computer Science - Information Theory, Mathematics - Combinatorics
Abstract

We introduce the class of multiply constant-weight codes to improve the reliability of certain physically unclonable function (PUF) response. We extend classical coding methods to construct multiply constant-weight codes from known $q$-ary and constant-weight codes. Analogues of Johnson bounds are derived and are shown to be asymptotically tight to a constant factor under certain conditions. We also examine the rates of the multiply constant-weight codes and interestingly, demonstrate that these rates are the same as those of constant-weight codes of suitable parameters. Asymptotic analysis of our code constructions is provided.

Published
2014

45. A Secure Asynchronous FPGA Architecture, Experimental Results and Some Debug Feedback

Author

Chaudhuri, Sumanta, Guilley, Sylvain, Hoogvorst, Philippe, Danger, Jean-Luc, Beyrouthy, Taha, Razafindraibe, Alin, Fesquet, Laurent, and Renaudin, Marc

Subjects
Computer Science - Hardware Architecture, Computer Science - Cryptography and Security
Abstract

This article presents an asynchronous FPGA architecture for implementing cryptographic algorithms secured against physical cryptanalysis. We discuss the suitability of asynchronous reconfigurable architectures for such applications before proceeding to model the side channel and defining our objectives. The logic block architecture is presented in detail. We discuss several solutions for the interconnect architecture, and how these solutions can be ported to other flavours of interconnect (i.e. single driver). Next We discuss in detail a high speed asynchronous configuration chain architecture used to configure our asynchronous FPGA with simulation results, and we present a 3 X 3 prototype FPGA fabricated in 65 nm CMOS. Lastly we present experiments to test the high speed asynchronous configuration chain and evaluate how far our objectives have been achieved with proposed solutions, and we conclude with emphasis on complementary FPGA CAD algorithms, and the effect of CMOS variation on Side-Channel Vulnerability., Comment: 18 Pages, 23 figures. In detail description of a 3X3 Aysnchronous FPGA Tape-Out

Published
2011

48. A Reconfigurable Programmable Logic Block for a Multi-Style Asynchronous FPGA resistant to Side-Channel Attacks

Author

Hoogvorst, Philippe, Guilley, Sylvain, Chaudhuri, Sumanta, Danger, Jean-Luc, Beyrouthy, Taha, and Fesquet, Laurent

Subjects
Computer Science - Cryptography and Security, Computer Science - Other Computer Science, D.4.6, B.2.1
Abstract

Side-channel attacks are efficient attacks against cryptographic devices. They use only quantities observable from outside, such as the duration and the power consumption. Attacks against synchronous devices using electric observations are facilitated by the fact that all transitions occur simultaneously with some global clock signal. Asynchronous control remove this synchronization and therefore makes it more difficult for the attacker to insulate \emph{interesting intervals}. In addition the coding of data in an asynchronous circuit is inherently more difficult to attack. This article describes the Programmable Logic Block of an asynchronous FPGA resistant against \emph{side-channel attacks}. Additionally it can implement different styles of asynchronous control and of data representation., Comment: 29 pages

Published
2008

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