Publications associées à Système de détection d'intrusion

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

X-Attack 2.0: The Risk of Power Wasters and Satisfiability Don’t-Care Hardware Trojans to Shared Cloud FPGAs

Mirjana Stojilovic, Dina Gamaleldin Ahmed Shawky Mahmoud, Beatrice Shokry Samir Shokry, Wei Hu

Cloud computing environments increasingly provision field-programmable gate arrays (FPGAs) for their programmability and hardware-level parallelism. While FPGAs are typically used by one tenant at a time, multitenant schemes supporting spatial sharing of c ...

2024

On the Theory and Practice of Modern Secure Messaging

Daniel Patrick Collins

Billions of people now have conversations daily over the Internet. A large portion of this communication takes place via secure messaging protocols that offer "end-to-end encryption'" guarantees and resilience to compromise like the widely-used Double Ratc ...

EPFL2024

Improving Deepfake Detectors against Real-world Perturbations with Amplitude-Phase Switch Augmentation

Touradj Ebrahimi, Yuhang Lu, Ruizhi Luo

In recent years, the remarkable progress in facial manipulation techniques has raised social concerns due to their potential malicious usage and has received considerable attention from both industry and academia. While current deep learning-based face for ...

2023

Anonymous Tokens with Stronger Metadata Bit Hiding from Algebraic MACs

Serge Vaudenay, Fatma Betül Durak

On the one hand, the web needs to be secured from malicious activities such as bots or DoS attacks; on the other hand, such needs ideally should not justify services tracking people's activities on the web. Anonymous tokens provide a nice tradeoff between ...

2023

Security Measures for Grids Against Rank-1 Undetectable Time-Synchronization Attacks

Jean-Yves Le Boudec, Marguerite Marie Nathalie Delcourt

Time-synchronization attacks on phasor measurement units (PMUs) pose a real threat to smart grids; it was shown that they are feasible in practice and that they can have a nonnegligible negative impact on state estimation, without triggering the bad data d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Designing a Provenance Analysis for SGX Enclaves

Mathias Josef Payer, Flavio Toffalini

SGX enclaves are trusted user-space memory regions that ensure isolation from the host, which is considered malicious. However, enclaves may suffer from vulnerabilities that allow adversaries to compromise their trustworthiness. Consequently, the SGX isola ...

ASSOC COMPUTING MACHINERY2022

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Sébastien Marcel, Alexander Carl Unnervik

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how steal ...

IEEE2022

Annotation-efficient image anomaly detection

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

The present invention proposes a method for detecting anomalous or out-of-distribution images in a machine learning system (1) comprising a pre-training network with a first encoder, and an anomaly detection network with a second encoder. The system is fir ...

2022

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Javier Alejandro Maroto Morales

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Jiancheng Yang, Bo Du, Rui Xu

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vita ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Safeguarding the IoT From Malware Epidemics: A Percolation Theory Approach

Ainur Zhaikhan

The upcoming Internet of Things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devic ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Time-Synchronization Attacks against Critical Infrastructures and their Mitigation

Marguerite Marie Nathalie Delcourt

This work focuses on the security of critical infrastructures against time-synchronization attacks (TSA). A TSA can impact any network that relies on the dynamic analysis of data, by altering the time synchronization between its nodes. Such attacked networ ...

EPFL2021

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project

Mihai Adrian Ionescu, Thomas Ernst

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare ...

2021

A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids

Giancarlo Ferrari Trecate, Mustafa Sahin Turan

DC microgrids often present a hierarchical control architecture, requiring integration of communication layers. This leads to the possibility of malicious attackers disrupting the overall system. Motivated by this application, in this article, we present a ...

2020

WatchNet plus plus : efficient and accurate depth-based network for detecting people attacks and intrusion

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...

2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

A large body of research has focused on adversarial attacks which require to modify all input features with small l2- or l∞-norms. In this paper we instead focus on query-efficient sparse attacks in the black-box setting. Our versatile framework, Sparse-RS ...

2020

X-Attack: Remote Activation of Satisfiability Don’t-Care Hardware Trojans on Shared FPGAs

Mirjana Stojilovic, Dina Gamaleldin Ahmed Shawky Mahmoud, Wei Hu

Albeit very appealing, FPGA multitenancy in the cloud computing environment is currently on hold due to a number of recently discovered vulnerabilities to side-channel attacks and covert communication. In this work, we successfully demonstrate a new attack ...

IEEE2020

Detection and Measurement of Matrix Discontinuities in UHPFRC by Means of Distributed Fiber Optics Sensing

Eugen Brühwiler, Bartlomiej Wojciech Sawicki, Antoine Bassil

Following the significant improvement in their properties during the last decade, Distributed Fiber Optics sensing (DFOs) techniques are nowadays implemented for industrial use in the context of Structural Health Monitoring (SHM). While these techniques ha ...

2020

Employing Attack Graphs for Intrusion Detection

Mathias Josef Payer

Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must e ...

ASSOC COMPUTING MACHINERY2019