IMPLEMENTATION OF A HARDWARE TROJAN CHIP DETECTOR MODEL USING ARDUINO MICROCONTROLLER
Kadeejah ABDULSALAM
kabdulsalam@unilag.edu.ngUniversity of Lagos, Electrical Electronics and Computer Engineering Department (Nigeria)
John ADEBISI
Electrical Electronics and Computer Engineering Department (Nigeria)
Victor DUROJAIYE
* University of Lagos, Electrical Electronics and Computer Engineering Department (Nigeria)
Abstract
These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba¬bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training.
Keywords:
hardware trojan, chips, logic test, machine learning, microcontrollerReferences
Bai, X. (2018). Text classification based on LSTM and attention. 2018 Thirteenth International Conference on Digital Information Management (ICDIM) (pp. 29–32). IEEE. https://doi.org/10.1109/ICDIM.2018.8847061
DOI: https://doi.org/10.1109/ICDIM.2018.8847061
Google Scholar
Chen, X., Wang, L., Wang, Y., Liu, Y., & Yang, H. (2016). A general framework for hardware trojan detection in digital circuits by statistical learning algorithms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (vol. 36, no. 10, pp. 1633–1646). IEEE. https://doi.org/10.1109/TCAD.2016.2638442
DOI: https://doi.org/10.1109/TCAD.2016.2638442
Google Scholar
Cui, Q., Sun, K., Wang, S., Zhang, L., & Li, D. (2016). Hardware trojan detection based on cluster analysis of mahalanobis distance. 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (pp. 234–238). IEEE. https://doi.org/10.1109/IHMSC.2016.65
DOI: https://doi.org/10.1109/IHMSC.2016.65
Google Scholar
Grus, J. (2015). Data Science from Scratch. 1005 Gravenstein Highway North. O’Reilly Media.
Google Scholar
He, C., Hou, B., Wang, L., En, Y., & Xie, S. (2014). A novel hardware Trojan detection method based on side-channel analysis and PCA algorithm. 2014 10th International Conference on Reliability, Maintainability and Safety (ICRMS) (pp. 1043–1046). IEEE. https://doi.org/10.1109/ICRMS.2014.7107362
DOI: https://doi.org/10.1109/ICRMS.2014.7107362
Google Scholar
Iwase, T., Nozaki, Y., Yoshikawa, M., & Kumaki, T. (2015). Detection technique for hardware Trojans using machine learning in frequency domain. 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (pp. 185–186). IEEE. https://doi.org/10.1109/GCCE.2015.7398569
DOI: https://doi.org/10.1109/GCCE.2015.7398569
Google Scholar
Jahan, I., Sajal, S. Z., & Nygard, K. E. (2019). Prediction model using recurrent neural networks. 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 1–6) IEEE. https://doi.org/10.1109/EIT.2019.8834336
DOI: https://doi.org/10.1109/EIT.2019.8834336
Google Scholar
Ni, L., Li, S., Chen, J., Wei, P., & Zhao, Z. (2014). The influence on sensitivity of hardware trojans detection by test vector. 2014 Communications Security Conference (CSC 2014) (pp. 1–6). IEEE. https://doi.org/10.1049/cp.2014.0756
DOI: https://doi.org/10.1049/cp.2014.0756
Google Scholar
Paul, L. C., Suman, A. A., & Sultan, N. (2013). Methodological analysis of principal component analysis (PCA) method. International Journal of Computational Engineering & Management, 16(2), 32–38.
Google Scholar
Tutorial Point. (2020). Retrieved October 8, 2021 from https://www.tutorialspoint.com
Google Scholar
Salmani, H., Tehranipoor, M., & Plusquellic, J. (2011). A novel technique for improving hardware trojan detection and reducing trojan activation time. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 20(1), 112–125. https://doi.org/10.1109/TVLSI.2010.2093547
DOI: https://doi.org/10.1109/TVLSI.2010.2093547
Google Scholar
Shende, R., & Ambawade, D. D. (2016). A side channel based power analysis technique for hardware trojan detection using statistical learning approach. 2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN) (pp. 1–4). IEEE. https://doi.org/10.1109/WOCN.2016.7759894
DOI: https://doi.org/10.1109/WOCN.2016.7759894
Google Scholar
Wang, L.-W., & Luo, H.-W. (2011). A power analysis based approach to detect Trojan circuits. 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (pp. 380–384). IEEE. https://doi.org/10.1109/ICQR2MSE.2011.5976635
DOI: https://doi.org/10.1109/ICQR2MSE.2011.5976635
Google Scholar
Zhang, L., Sun, K., Cui, Q., Wang, S., Li, X., & Di, J. (2016). Multi adaptive hardware Trojan detection method based on power characteristics template. 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 414–418). IEEE. https://doi.org/10.1109/CCIS.2016.7790294
DOI: https://doi.org/10.1109/CCIS.2016.7790294
Google Scholar
Authors
Kadeejah ABDULSALAMkabdulsalam@unilag.edu.ng
University of Lagos, Electrical Electronics and Computer Engineering Department Nigeria
Authors
John ADEBISIElectrical Electronics and Computer Engineering Department Nigeria
Authors
Victor DUROJAIYE* University of Lagos, Electrical Electronics and Computer Engineering Department Nigeria
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