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Hardware IP Classification through W...

McGeehan, Brendan.

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Hardware IP Classification through Weighted Characteristics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hardware IP Classification through Weighted Characteristics./
Author:	McGeehan, Brendan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	36 p.
Notes:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13865724
ISBN:	9781392121481

Hardware IP Classification through Weighted Characteristics.
McGeehan, Brendan.

Hardware IP Classification through Weighted Characteristics. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 36 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.Cmp.E.)--University of Arkansas, 2019.

This item must not be added to any third party search indexes.

Today's business model for hardware designs frequently incorporates third-party Intellectual Property (IP) due to the many benefits it can bring to a company. For instance, outsourcing certain components of an overall design can reduce time-to-market by allowing each party to specialize and perfect a specific part of the overall design. However, allowing third-party involvement also increases the possibility of malicious attacks, such as hardware Trojan insertion. Trojan insertion is a particularly dangerous security threat because testing the functionality of an IP can often leave the Trojan undetected. Therefore, this thesis work provides an improvement on a Trojan detection method known as Structural Checking which analyzes Register-Transfer Level (RTL) and gate-level soft IPs. Given an unknown IP, the Structural Checking tool will break down the design primary ports and internal signals into assets that fall into six characteristics. These characteristics organize how the IP is structured and provide information about the unknown IP's overall function. The tool also provides a library of known designs referred to as the Golden Reference Library (GRL). All entries in the library are also broken down into the same six characteristics and are either known to be clean or known to have a Trojan inserted. An overall percent match for each library entry against the unknown IP is calculated by first computing a percent match within each characteristic. A weighted average of these percent matches makes up the final percentage. If the library entry with the best match is known to have a Trojan inserted, then the unknown design is likely to have a Trojan as well and vice versa. Due to the structural variability of soft IP designs, it is vital to provide the best possible weighting of the six characteristics to best match the unknown IP to the most similar library entry. This thesis work provides a statistical approach to finding the best weights to optimize the Structural Checking tool's matching algorithm.

ISBN: 9781392121481Subjects--Topical Terms:

1567821
Computer Engineering.

Hardware IP Classification through Weighted Characteristics.
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300 $a 36 p.
500 $a Source: Masters Abstracts International, Volume: 80-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Di, Jia.
502 $a Thesis (M.S.Cmp.E.)--University of Arkansas, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Today's business model for hardware designs frequently incorporates third-party Intellectual Property (IP) due to the many benefits it can bring to a company. For instance, outsourcing certain components of an overall design can reduce time-to-market by allowing each party to specialize and perfect a specific part of the overall design. However, allowing third-party involvement also increases the possibility of malicious attacks, such as hardware Trojan insertion. Trojan insertion is a particularly dangerous security threat because testing the functionality of an IP can often leave the Trojan undetected. Therefore, this thesis work provides an improvement on a Trojan detection method known as Structural Checking which analyzes Register-Transfer Level (RTL) and gate-level soft IPs. Given an unknown IP, the Structural Checking tool will break down the design primary ports and internal signals into assets that fall into six characteristics. These characteristics organize how the IP is structured and provide information about the unknown IP's overall function. The tool also provides a library of known designs referred to as the Golden Reference Library (GRL). All entries in the library are also broken down into the same six characteristics and are either known to be clean or known to have a Trojan inserted. An overall percent match for each library entry against the unknown IP is calculated by first computing a percent match within each characteristic. A weighted average of these percent matches makes up the final percentage. If the library entry with the best match is known to have a Trojan inserted, then the unknown design is likely to have a Trojan as well and vice versa. Due to the structural variability of soft IP designs, it is vital to provide the best possible weighting of the six characteristics to best match the unknown IP to the most similar library entry. This thesis work provides a statistical approach to finding the best weights to optimize the Structural Checking tool's matching algorithm.
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