Detecting Compromised Hardware Integrity with Machine Learning in Multicore Processors

Due to the globalization of the Integrated Circuits (IC) manufacturing process, the trust surrounding hardware reliability has become compromised through methods such as injection of malicious hardware (including hardware trojans), hardware backdoors, IP theft, and counterfeit hardware. This thesis will focus on the issue of counterfeit hardware. As the complexity of modern day hardware has increased, detecting counterfeits has become increasingly difficult. Current methods of physical and electrical inspection are limited in both depth and size limitations. Physical inspections are only useful for identifying counterfeits that manifest physically, while electrical testing requires expensive and extensive setups. Computational evaluation of all possible states of the device can at times be considered unfeasible. This the- sis proposes the development of a method in which internal data traffic within the system is compared to a known golden device through the use of machine learning techniques. The technique utilized in this research will be One Class Support Vector Machine (OC-SVM) analysis. This is due to its ability to be trained on only one class of data for its functionality, and can separate out anomalous classes without being trained on that data. The system will be evaluated across a number of different benchmark programs, each with it’s own respective model trained on the known good system. By analyzing the results of the model, accuracy metrics can be used to indicate a models ability to separate the data from the two system states. The success of this method would pose another feasible method of counterfeit detection that wouldn’t be hindered by the limitations of current methods.