Unsupervised Detection of Adversarial Collaboration in Data-Driven Networking - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Unsupervised Detection of Adversarial Collaboration in Data-Driven Networking

Matteo Sammarco
  • Function : Author
  • PersonId : 1084026
Marcin Detyniecki
Marcelo Dias de Amorim

Abstract

Data-driven networking in combination with machine learning is a powerful way to design and manage networked systems. In this paper, we consider the case of participatory collection of wireless traffic, which is an inexpensive way to infer the wireless activity in a locality. Since such a type of measurement system leans on the goodwill of the end users, it opens a new venue for malicious actions. Possible consequences of attacks are changes in the underlying communication substrate or even the collapse of the network. We assess the influence of these adversaries by identifying possible hostile actions and propose a method to detect them based on unsupervised machine learning models. Through an experimental campaign in various scenarios, we show that attacks with critical impacts are systematically detected, while unidentified attacks produce only a negligible impact in the measurement system.
Fichier principal
Vignette du fichier
hal-02348767.pdf (3.62 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03036149 , version 1 (02-12-2020)

Identifiers

  • HAL Id : hal-03036149 , version 1

Cite

Matteo Sammarco, Miguel Elias Mitre Campista, Marcin Detyniecki, Tahiry Razafindralambo, Marcelo Dias de Amorim. Unsupervised Detection of Adversarial Collaboration in Data-Driven Networking. 2019 10th International Conference on Networks of the Future (NoF), Oct 2019, Rome, Italy. ⟨hal-03036149⟩
140 View
62 Download

Share

Gmail Facebook Twitter LinkedIn More