Search Results for author: Vincent Lenders

Found 6 papers, 3 papers with code

Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing

no code implementations6 Sep 2022 Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé, Francesco Marino, Jean-Philippe Bossuat, Vincent Lenders, Jean-Pierre Hubaux

However, this activity presents challenges due to the tension between data sharing and confidentiality, that result in information retention often leading to a free-rider problem.

Privacy Preserving

From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach

no code implementations9 Dec 2021 Chi Thang Duong, Dimitri Percia David, Ljiljana Dolamic, Alain Mermoud, Vincent Lenders, Karl Aberer

This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies.

Language Modelling Retrieval

QPEP: A QUIC-Based Approach to Encrypted Performance Enhancing Proxies for High-Latency Satellite Broadband

2 code implementations12 Feb 2020 James Pavur, Martin Strohmeier, Vincent Lenders, Ivan Martinovic

However, status-quo services are often unencrypted by default and vulnerable to eavesdropping attacks.

Cryptography and Security Networking and Internet Architecture Performance

Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning

no code implementations30 Jul 2019 Martin Strohmeier, Matthew Smith, Vincent Lenders, Ivan Martinovic

Classi-Fly obtains the correct aircraft category with an accuracy of over 88%, demonstrating that it can improve the meta data necessary for applications working with air traffic communication.

BIG-bench Machine Learning Stock Market Prediction +1

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

1 code implementation22 Jul 2018 Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use.

Anomaly Detection Data Compression

Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

1 code implementation27 Jul 2017 Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano, Vincent Lenders, Sofie Pollin

We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates.

Networking and Internet Architecture

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