Traffic Fingerprinting Attacks on Internet of Things using Machine Learning

M Skowron , Artur Janicki , Wojciech Mazurczyk

Abstract

The Internet of Things (IoT) concept has been widely adopted and Internet connected devices enter more and more areas of our everyday lives. However, their limited security measures raise increasing concerns, especially in terms of users’ privacy. That is why, in this paper, privacy risks, focusing primarily on information leakage exposed by traffic fingerprinting attacks, on IoT devices are investigated. The considered attacks take advantage of the statistical network flows’ features and application of machine learning (ML) methods and can be utilized by a passive traffic observer. In this perspective, the first part of the research presented in this paper analyzes the feasibility of identifying individual devices in a victim’s home network. It considers smart environment setups of different scales and conditions, and it also includes a performance comparison of the different ML models applied. The second part introduces and validates a method for the devices’ state detection based on pattern recognition with ML. Finally, recommendations for mitigating the discussed privacy risks are also enclosed.
Author M Skowron
M Skowron,,
-
, Artur Janicki (FEIT / IT)
Artur Janicki,,
- The Institute of Telecommunications
, Wojciech Mazurczyk (FEIT / ICS)
Wojciech Mazurczyk,,
- The Institute of Computer Science
Journal seriesIEEE Access, ISSN 2169-3536
Issue year2020
Vol8
Pages1-15
Publication size in sheets0.3
Keywords in EnglishInternet of Things , machine learning , network traffic fingerprinting , privacy , traffic analysis
ASJC Classification1700 General Computer Science; 2200 General Engineering; 2500 General Materials Science
DOIDOI:10.1109/ACCESS.2020.2969015
URL https://ieeexplore.ieee.org/document/8967051
Languageen angielski
File
ACCESS2969015(2)-1.pdf 1.28 MB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 18-09-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; GS Citations = 2.0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.718; WoS Impact Factor: 2018 = 4.098 (2) - 2018=4.54 (5)
Citation count*2 (2020-09-10)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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