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.
","abstract_html":"<p>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. </p>","abstract_has_math":false,"creators":["McGeehan, Brendan"],"institution":null,"degree_name":"Master of Science in Computer Engineering (MSCmpE)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Parkerson, James P.","Thompson, Dale R."],"advisors":["Di, Jia"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-05-01T07:00:00Z","date_published":"2019-05-01T07:00:00Z","updated_at":"2026-07-24T00:59:49Z","subjects":["Classifcation","Hardware","Trojans","Hardware Systems","Information Security","VLSI and Circuits, Embedded and Hardware Systems"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/3166","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Parkerson, James P.","Thompson, Dale R."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Di, Jia"]},{"key":"dc:creator","label":"Author","values":["McGeehan, Brendan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-02-06T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Engineering (MSCmpE)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Classifcation","Hardware","Trojans","Hardware Systems","Information Security","VLSI and Circuits, Embedded and Hardware Systems"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/3166"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Hardware IP Classification through Weighted Characteristics"]}]}],"canonical_facts":{"dc:contributor":["Parkerson, James P.","Thompson, Dale R."],"dc:contributor.advisor":["Di, Jia"],"dc:creator":["McGeehan, Brendan"],"dc:date":["2019"],"dc:date.available":["2024-02-06T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/3166"],"dc:subject":["Classifcation","Hardware","Trojans","Hardware Systems","Information Security","VLSI and Circuits, Embedded and Hardware Systems"],"dc:title":["Hardware IP Classification through Weighted Characteristics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Engineering (MSCmpE)"]},"updated_at":"2026-07-24T00:59:49Z"}