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Exploring ICMetrics to detect abnormal program behaviour on embedded devices

Zhai, Xiaojun, Appiah, Kofi, Ehsan, Shoaib, Howells, Gareth, Hu, Huosheng, Gu, Dongbing, McDonald-Maier, Klaus (2015) Exploring ICMetrics to detect abnormal program behaviour on embedded devices. Journal of Systems Architecture, 61 (10). pp. 567-575. ISSN 1383-7621. (doi:10.1016/j.sysarc.2015.07.007) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.sysarc.2015.07.007

Abstract

Execution of unknown or malicious software on an embedded system may trigger harmful system behaviour targeted at stealing sensitive data and/or causing damage to the system. It is thus considered a potential and significant threat to the security of embedded systems. Generally, the resource constrained nature of commercial off-the-shelf (COTS) embedded devices, such as embedded medical equipment, does not allow computationally expensive protection solutions to be deployed on these devices, rendering them vulnerable. A Self-Organising Map (SOM) based and Fuzzy C-means based approaches are proposed in this paper for detecting abnormal program behaviour to boost embedded system security. The presented technique extracts features derived from processor’s Program Counter (PC) and Cycles per Instruction (CPI), and then utilises the features to identify abnormal behaviour using the SOM. Results achieved in our experiment show that the proposed SOM based and Fuzzy C-means based methods can identify unknown program behaviours not included in the training set with 90.9% and 98.7% accuracy.

Item Type: Article
DOI/Identification number: 10.1016/j.sysarc.2015.07.007
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 26 Nov 2015 13:14 UTC
Last Modified: 29 May 2019 16:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/52454 (The current URI for this page, for reference purposes)
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