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Please use this identifier to cite or link to this item: http://10.10.120.238:8080/xmlui/handle/123456789/204
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dc.contributor.authorKumar G.en_US
dc.contributor.authorRiaz A.en_US
dc.contributor.authorPrasad Y.en_US
dc.contributor.authorAhlawat S.en_US
dc.date.accessioned2023-11-30T08:13:17Z-
dc.date.available2023-11-30T08:13:17Z-
dc.date.issued2022-
dc.identifier.isbn978-1450393225-
dc.identifier.otherEID(2-s2.0-85131695670)-
dc.identifier.urihttps://dx.doi.org/10.1145/3526241.3530370-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/204-
dc.description.abstractThe IEEE 1687 standard, which is commonly used for efficient access of on-chip instruments, could be exploited by an intruder and thus needs to be secured. One of the techniques to alleviate the vulnerability of 1687 network is to use a secure access protocol that is based on licensed access software, Chip ID and locking SIB. A licensed access software is generally used to gain control of the embedded instruments and use them as per requirement. In this paper, a successful attack using various machine learning algorithms has been instigated on secure access protocol scheme. It is demonstrated that machine learning algorithms have the potential of breaching the secure communication between the access software and the board and hence access the sensitive instruments. Furthermore, Random Forest significantly outperforms the other models in terms of breaking the security. © 2022 ACM.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.sourceProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSIen_US
dc.subjectboard securityen_US
dc.subjectIEEE 1687en_US
dc.subjectinstrumentsen_US
dc.subjectLSIBen_US
dc.subjectmachine learningen_US
dc.titleOn Attacking Locking SIB based IJTAG Architectureen_US
dc.typeConference Paperen_US
Appears in Collections:Conference Paper

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