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dc.contributor.authorZhai, Xiaojun
dc.contributor.authorEhsan, Shoaib
dc.contributor.authorHowells, Gareth
dc.contributor.authorDongbing, Gu
dc.contributor.authorMcDonald-Maier, Klaus
dc.contributor.authorAppiah, Kofi
dc.contributor.authorHu, Huosheng
dc.date.accessioned2015-12-14T09:38:06Z
dc.date.available2015-12-14T09:38:06Zen
dc.date.issued2015-04-13
dc.identifier.citationZhai, X, Appiah, K, Ehsan, S, Howells, G, Hu, H, Gu, D, & McDonald-Maier, K (2015), 'A Method for Detecting Abnormal Program Behavior on Embedded Devices', IEEE Transactions On Information Forensics And Security, 10, 8, pp. 1692-1704en
dc.identifier.issn1556-6013
dc.identifier.doi10.1109/TIFS.2015.2422674
dc.identifier.urihttp://hdl.handle.net/10545/583863
dc.description.abstractA potential threat to embedded systems is the execution of unknown or malicious software capable of triggering harmful system behavior, aimed at theft of sensitive data or causing damage to the system. Commercial off-the-shelf embedded devices, such as embedded medical equipment, are more vulnerable as these type of products cannot be amended conventionally or have limited resources to implement protection mechanisms. In this paper, we present a self-organizing map (SOM)-based approach to enhance embedded system security by detecting abnormal program behavior. The proposed method extracts features derived from processor's program counter and cycles per instruction, and then utilises the features to identify abnormal behavior using the SOM. Results achieved in our experiment show that the proposed method can identify unknown program behaviors not included in the training set with over 98.4% accuracy.
dc.description.sponsorshipIEEE Signal Processing Societyen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesINSPEC Accession Number: 15232972en
dc.relation.ispartofseriesVol. 10en
dc.relation.ispartofseriesIssue 8en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7084637en
dc.rightsArchived with thanks to IEEE Transactions on Information Forensics and Securityen
dc.subjectICMetricsen
dc.subjectEmbedded systemsen
dc.subjectSelf-organising mapen
dc.subjectIntrusion detectionen
dc.titleA method for detecting abnormal program behavior on embedded devicesen
dc.typeArticleen
dc.identifier.eissn1556-6021
dc.contributor.departmentUniversity of Leicesteren
dc.contributor.departmentUniversity of Essexen
dc.contributor.departmentUniversity of Kenten
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalIEEE Transactions on Information Forensics and Securityen
html.description.abstractA potential threat to embedded systems is the execution of unknown or malicious software capable of triggering harmful system behavior, aimed at theft of sensitive data or causing damage to the system. Commercial off-the-shelf embedded devices, such as embedded medical equipment, are more vulnerable as these type of products cannot be amended conventionally or have limited resources to implement protection mechanisms. In this paper, we present a self-organizing map (SOM)-based approach to enhance embedded system security by detecting abnormal program behavior. The proposed method extracts features derived from processor's program counter and cycles per instruction, and then utilises the features to identify abnormal behavior using the SOM. Results achieved in our experiment show that the proposed method can identify unknown program behaviors not included in the training set with over 98.4% accuracy.


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