Publications associées à Deep packet inspection

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

EPFL2023

Global, Passive Detection of Connection Tampering

Louis-Henri Manuel Jakob Merino

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Assoc Computing Machinery2023

On Calculating the Minimum Rate for the Cooperative Data Exchange Problem Over Fully Connected Networks

Michael Christoph Gastpar, Su Li

We study the cooperative data exchange problem for fully connected networks. In this problem, nodes make broadcast transmissions to recover a file consisting of K independent packets. Each node initially only possesses a subset of the packets. We propose ( ...

2021

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Pascal Fua, Mathieu Salzmann, Agata Justyna Mosinska, Mateusz Kozinski

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

2020

Backpropagation-Friendly Eigendecomposition

Pascal Fua, Mathieu Salzmann, Zheng Dang, Wei Wang, Yinlin Hu

Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it with the Power Iteration method, particularly when dealing with large matri ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Rachid Guerraoui, Anne-Marie Kermarrec, Antoine Rault, Davide Frey, Florestan Laurentin Marie De Moor

Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recomme ...

IEEE2018

A Sensor Network Architecture for Digital SiPM-Based PET Systems

Edoardo Charbon, Claudio Bruschini, Francesco Gramuglia

Digital silicon photo-multipliers (SiPMs) have emerged in the recent past as a viable low cost alternative to photomultiplier tubes in positron emission tomography systems, providing multiple timestamps, energy and scintillation coordinates at high spatial ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Automated Synthesis of Adversarial Workloads for Network Functions

Rishabh Ramesh Iyer, Aikaterini Argyraki, Arseniy Zaostrovnykh, Jonas Fietz, Luis David Figueiredo Mascarenhas Moreira Pedrosa

Software network functions promise to simplify the deployment of network services and reduce network operation cost. However, they face the challenge of unpredictable performance. Given this performance variability, it is imperative that during deployment, ...

ACM2018

Multiple Objectives of Lawful-Surveillance Protocols

Bryan Alexander Ford

In recent work on open, privacy-preserving, accountable surveillance, we have proposed the use of cryptographic protocols that enable law-enforcement and intelligence agencies to obtain actionable information about targeted users of mass-communication syst ...

2017

Data-Driven, Personalized Usable Privacy

Hamza Harkous

We live in the "inverse-privacy" world, where service providers derive insights from users' data that the users do not even know about. This has been fueled by the advancements in machine learning technologies, which allowed providers to go beyond the supe ...

EPFL2017