Malware detection and prevention in RFID systems

Fernando, Harinda and Abawajy, Jemal 2013, Malware detection and prevention in RFID systems. In Bessis, Nik, Xhafa, Fatos, Varvarigou, Dora, Hill, Richard and Li, Maozhen (ed), Internet of things and inter-cooperative computational technologies for collective intelligence, Springer, Heidelberg, Germany, pp.143-166, doi: 10.1007/978-3-642-34952-2_6.

Attached Files
Name Description MIMEType Size Downloads

Title Malware detection and prevention in RFID systems
Author(s) Fernando, Harinda
Abawajy, JemalORCID iD for Abawajy, Jemal
Title of book Internet of things and inter-cooperative computational technologies for collective intelligence
Editor(s) Bessis, Nik
Xhafa, Fatos
Varvarigou, Dora
Hill, Richard
Li, Maozhen
Publication date 2013
Series Studies in Computational Intelligence ; v.460
Chapter number 6
Total chapters 18
Start page 143
End page 166
Total pages 24
Publisher Springer
Place of Publication Heidelberg, Germany
Summary The threat that malware poses to RFID systems was identified only recently. Fortunately, all currently known RFID malware is based on SQLIA. Therefore, in this chapter we propose a dual pronged, tag based SQLIA detection and prevention method optimized for RFID systems. The first technique is a SQL query matching approach that uses simple string comparisons and provides strong security against a majority of the SQLIA types possible on RFID systems. To provide security against second order SQLIA, which is a major gap in the current literature, we also propose a tag data validation and sanitization technique. The preliminary evaluation of our query matching technique is very promising, showing 100% detection rates and 0% false positives for all attacks other than second order injection.
ISBN 3642349528
ISSN 1860-949X
Language eng
DOI 10.1007/978-3-642-34952-2_6
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category B1 Book chapter
Copyright notice ©2012, Springer
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 446 Abstract Views, 12 File Downloads  -  Detailed Statistics
Created: Mon, 26 Aug 2013, 15:35:56 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact