Concept
Sécurité du réseau
Publications associées (40)
Machine Learning Security Against Data Poisoning: Are We There Yet?
Poisoning attacks compromise the training data utilized to train machine learning (ML) models, diminishing their overall performance, manipulating predictions on specific test samples, and implanting backdoors. This article thoughtfully explores these atta ...
Ieee Computer Soc2024Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
Kathrin Grosse, Sebastiano Vascon
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that ...
2023Machine Learning Security in Industry: A Quantitative Survey
Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023HyperLogLog: Exponentially Bad in Adversarial Settings
Computing the count of distinct elements in large data sets is a common task but naive approaches are memory-expensive. The HyperLogLog (HLL) algorithm (Flajolet et al., 2007) estimates a data set's cardinality while using significantly less memory than a ...
IEEE COMPUTER SOC2022Annotation-efficient image anomaly detection
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 ...
2022Safeguarding the IoT From Malware Epidemics: A Percolation Theory Approach
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 INC2021LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads
Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei
In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...
MICROTOME PUBLISHING2021OrthoNet: Multilayer Network Data Clustering
Pascal Frossard, Mireille El Gheche, Giovanni Chierchia
Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Que Sera Consensus: Simple Asynchronous Agreement with Private Coins and Threshold Logical Clocks
Bryan Alexander Ford, Philipp Svetolik Jovanovic
It is commonly held that asynchronous consensus is much more complex, difficult, and costly than partially-synchronous algorithms, especially without using common coins. This paper challenges that conventional wisdom with que sera consensus QSC, an approac ...
2020