dc.contributor.authorJap, Dirmanto
dc.contributor.authorHe, Wei
dc.contributor.authorBhasin, Shivam
dc.identifier.citationJap, D., He, W., & Bhasin, S. (2016). Supervised and unsupervised machine learning for side-channel based Trojan detection. 2016 IEEE 27th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 17-24.en_US
dc.description.abstractHardware Trojan (HT) has recently drawn much attention in both industry and academia due to the global outsourcing trend in semiconductor manufacturing, where a malicious logic can be inserted into the security critical ICs at almost any stages. HT severity mainly stems from its low-cost and stealthy nature where the HT only functions at a strict condition to purposely alter the logic or physical behavior for leaking secrets. This fact makes HT detection very challenging in practice. In this paper, we propose a novel HT detection technique based on machine learning approach. The described solution is constructed over one-class SVM and is shown to be more robust compared to the template based detection techniques. An unsupervised approach is also applied in our solution for mitigating the golden model dependencies. To evaluate the solution, a practical HT design was inserted into an AES coprocessor implemented in a Xilinx FPGA. Based on the partial reconfiguration, the HT size can be dynamically changed without altering cipher part, which helps to precisely evaluate the HT influence. The experimental results have shown that our proposed detection technique achieve a high performance accuracy.en_US
dc.format.extent8 p.en_US
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ASAP.2016.7760768].en_US
dc.subjectSupport vector machinesen_US
dc.subjectTraining dataen_US
dc.titleSupervised and unsupervised machine learning for side-channel based Trojan detectionen_US
dc.typeConference Paper
dc.contributor.conference2016 IEEE 27th International Conference on Application-specific Systems, Architectures and Processors (ASAP)en_US
dc.contributor.researchTemasek Laboratoriesen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.versionAccepted versionen_US

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