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A Self-Organising Map Based Algorithm for Analysis of ICmetrics Features

Zhai, Xiaojun, Appiah, Kofi, Ehsan, Shoaib, Cheung, Wah M., Hu, Huosheng, Gu, Dongbing, McDonald-Maier, Klaus D., Howells, Gareth (2013) A Self-Organising Map Based Algorithm for Analysis of ICmetrics Features. In: Emerging Security Technologies (EST), 2013 Fourth International Conference on. . pp. 93-97. IEEE (doi:10.1109/EST.2013.22) (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/10.1109/EST.2013.22

Abstract

ICmetrics is a new approach that exploits the characteristic and behaviour of an embedded system to obtain a collection of properties and features, which aims to uniquely identify and secure an embedded system based on its own behavioural identity. In this paper, an algorithm based on a self-organising map (SOM) neural network is proposed to extract and analyse the features derived from a processor's performance profile (i.e. average cycles per instruction (CPI)), where the extracted features are used to help finding the main behaviours of the system. The proposed algorithm has been tested with different programs selected from the MiBench benchmark suite, and the results achieved show that it can successfully segment each program into different main phases based on the unique extracted features, which confirms its utility for the ICmetrics technology.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EST.2013.22
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 10 Jun 2014 08:41 UTC
Last Modified: 29 May 2019 12:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41369 (The current URI for this page, for reference purposes)
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