University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Unsupervised Learning for Trustworthy IoT

Banerjee, Nikhil, Giannetsos, Thanassis, Panaousis, Emmanouil and Cheong Took, Clive (2018) Unsupervised Learning for Trustworthy IoT In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), 08-13 Jul 2018, Rio de Janeiro, Brazil.

[img]
Preview
Text
Unsupervised Learning for Trustworthy IoT.pdf

Download (1MB) | Preview

Abstract

The advancement of Internet-of-Things (IoT) edge devices with various types of sensors enables us to harness diverse information with Mobile Crowd-Sensing applications (MCS). This highly dynamic setting entails the collection of ubiquitous data traces, originating from sensors carried by people, introducing new information security challenges; one of them being the preservation of data trustworthiness. What is needed in these settings is the timely analysis of these large datasets to produce accurate insights on the correctness of user reports. Existing data mining and other artificial intelligence methods are the most popular to gain hidden insights from IoT data, albeit with many challenges. In this paper, we first model the cyber trustworthiness of MCS reports in the presence of intelligent and colluding adversaries. We then rigorously assess, using real IoT datasets, the effectiveness and accuracy of well-known data mining algorithms when employed towards IoT security and privacy. By taking into account the spatiotemporal changes of the underlying phenomena, we demonstrate how concept drifts can masquerade the existence of attackers and their impact on the accuracy of both the clustering and classification processes. Our initial set of results clearly show that these unsupervised learning algorithms are prone to adversarial infection, thus, magnifying the need for further research in the field by leveraging a mix of advanced machine learning models and mathematical optimization techniques.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Banerjee, Nikhil
Giannetsos, Thanassis
Panaousis, Emmanouile.panaousis@surrey.ac.uk
Cheong Took, Clivec.cheongtook@surrey.ac.uk
Date : 14 July 2018
DOI : 10.1109/FUZZ-IEEE.2018.8491672
Copyright Disclaimer : © The Author(s) 2018
Uncontrolled Keywords : Machine learning; Classification; Mobile Crowd-Sensing; Data trustworthiness
Related URLs :
Depositing User : Clive Harris
Date Deposited : 27 Apr 2018 10:00
Last Modified : 05 Nov 2018 14:44
URI: http://epubs.surrey.ac.uk/id/eprint/846319

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800