Open Access Open Access  Restricted Access Subscription or Fee Access

Guided Data Augmentation Scheme Combined with Adaptive Evolutionary Algorithm for Hardware Trojan Detection to Enhance Communication Security


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecap.v12i4.21578

Abstract


The security of the RS232 module plays a pivotal role in ensuring secure communication. Hardware Trojans can be used to gain root shell access to the RS232 modules to leak sensitive information or perform other malicious actions. As a result, it is crucial to detect the presence of hardware Trojans effectively. The work proposes a guided data augmentation scheme that facilitates enhanced synthetic data generation without any false positives. The proposed scheme uses instance hardness based under sampling to effectively remove data with less information and ADASYN upsampling algorithm to produce more realistic synthetic data. The work thus effectively utilizes the information present in majority and minority data instances effectively. In order to facilitate accurate Trojan detection, hyperparameters of the XGBoost model are optimized using genetic algorithm. The model produces zeros false positives and false negatives for all the test circuits.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Communication Security; Evolutionary Algorithm; Hardware Trojan Detection; Hybrid Sampling; XGBoost

Full Text:

PDF


References


Mohammed, H., Hasan, S.R. and Awwad, F., Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks. IEEE Access, Volume. 8,2020, pp.36847-36862.
https://doi.org/10.1109/ACCESS.2020.2975016

Gül, Ö., A Research on Cyber Security Intrusion Detection Against Physical Access Cyber Attacks Using Open Source Software for Smart Grids, (2021) International Review of Electrical Engineering (IREE), 16 (2), pp. 136-146.
https://doi.org/10.15866/iree.v16i2.19174

Seethal, C., Yamuna, B., Balasubramanian, K., Mishra, D., Nagaraj, N., Turbo Product Codes for Satellite Communication: a Survey, (2022) International Journal on Communications Antenna and Propagation (IRECAP), 12 (2), pp. 98-110.
https://doi.org/10.15866/irecap.v12i2.21440

H. Li, Q. Liu, and J. Zhang, A survey of hardware trojan threat and defense, Integration, Volume. 55, 2016. pp. 426-437.
https://doi.org/10.1016/j.vlsi.2016.01.004

Srinivas, C.G., Balachander, S., Singh Samant, Y.C., Hariharan, B.V. and Devi, M.N., Hardware Trojan Detection Using XGBoost Algorithm for IoT with Data Augmentation Using CTGAN and SMOTE. International Conference on Ubiquitous Communications and Network Computing, pp. 116-127. Springer, Cham. March 2021
https://doi.org/10.1007/978-3-030-79276-3_10

Priyatharishini, M., and M. Nirmala Devi. A compressive sensing algorithm for hardware trojan detection. International Journal of Electrical and Computer Engineering Volume. 9, 2019, pp: 4035-4043.
https://doi.org/10.11591/ijece.v9i5.pp4035-4043

Dong C, He G, Liu X, Yang Y, Guo W. A multi-layer hardware trojan protection framework for IoT chips, IEEE Access, February 2019 pp.23628-39.
https://doi.org/10.1109/ACCESS.2019.2896479

K. Hasegawa, M. Oya, M. Yanagisawa, and N. Togawa, Hardware trojans classification for gate-level netlists based on machine learning, IEEE 22nd International Symposium On-Line Testing and Robust System Design (IOLTS), 2016, pp. 203-206.
https://doi.org/10.1109/IOLTS.2016.7604700

Hasegawa K, Yanagisawa M and Togawa N. Trojan-net feature extraction and its application to hardware-Trojan detection for gate-level netlists using random forest. IEICE T Fund Electr, 2017, Volume 100, pp2857-2868.
https://doi.org/10.1587/transfun.E100.A.2857

Hasegawa K, Yanagisawa M and Togawa N. Hardware Trojans classification for gate-level netlists using multilayer neural networks. IEEE23rd international symposium on on-line testing and robust system design (IOLTS), Thessaloniki, 3-5,pp.227-232. New York, July 2017
https://doi.org/10.1109/IOLTS.2017.8046227

Dong, Chen, Jinghui Chen, Wenzhong Guo, and Jian Zou. A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things. International Journal of Distributed Sensor Networks 15, no. 12 (2019): 1550147719888098.
https://doi.org/10.1177/1550147719888098

C. H. Kok, C. Y. Ooi, M. Moghbel, N. Ismail, H. S. Choo, and M. Inoue, Classification of trojan nets based on scoap values using supervised learning, IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5,2019.
https://doi.org/10.1109/ISCAS.2019.8702462

Sharma, R., Valivati, N.K., Sharma, G.K. and Pattanaik, M., 2020, July. A New Hardware Trojan Detection Technique using Class Weighted XGBoost Classifier. 24th International Symposium on VLSI Design and Test (VDAT) (pp. 1-6),2020.
https://doi.org/10.1109/VDAT50263.2020.9190603

Priyadharshini M, Saravanan P, An Efficient Hardware Trojan Detection Approach adopting Testability based Features in Proc. IEEE Int. TestConf., pp. 1-5, Jul. 2020.
https://doi.org/10.1109/ITCIndia49857.2020.9171786

Naskar, P., Dhar, T., Roy, S. K.,Hardware Trojan Detection Using Improved Testability Measures, IEEE Int, Symp. on Devices, Circuits and Systems (ISDCS), pp. 1-6, Mar. 2020.
https://doi.org/10.1109/ISDCS49393.2020.9263026

Hasegawa, K., Yanagisawa, M. and Togawa, N.,A hardware-Trojan classification method utilizing boundary net structures, IEEE Int. Conf. on Consumer Electron. (ICCE), pp. 1-4, Jan. 2018.
https://doi.org/10.1109/ICCE.2018.8326247

Li, S., Zhang, Y., Chen, X., Ge, M., Mao, Z. and Yao, J. A XGBoost based Hybrid Detection Scheme for Gate-Level Hardware Trojan, IEEE 9th Joint Int. Inf. Technol. and Artificial Intelligence Conf. (ITAIC, pp. 41-47), Dec. 2020.
https://doi.org/10.1109/ITAIC49862.2020.9338932

Zhang, Y., Li, S., Chen, X., Yao, J., Mao, Z., Yang, J. and Hua, Y.,Hybrid multi-level hardware Trojan detection platform for gate-level netlists based on XGBoost, IET Comput. & Digital Techniques, vol. 16, no.(2-3), May.2022, pp.54-70.
https://doi.org/10.1049/cdt2.12040

Zhang, N., Lv, Z., Zhang, Y., Li, H., Zhang, Y. and Huang, W., Novel design of Hardware Trojan: A generic approach for defeating testability based detection. IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 162-173, December 2020.
https://doi.org/10.1109/TrustCom50675.2020.00034

Simulator, Synopsys. Design Compiler Reference Manual. (1994).

Chen, T. and Guestrin, C. (2016) XGBoost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, USA, pp.785-794, 2016.
https://doi.org/10.1145/2939672.2939785

Sriram, S., Vinayakumar, R., Alazab, M. and Soman, K.P., 2020, July. Network flow based IoT botnet attack detection using deep learning. IEEE INFOCOM IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 189-194, 2020.
https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162668

Sriram, S., Vinayakumar, R., Sowmya, V., Krichen, M., Noureddine, D.B., Shashank, A. and Soman, K.P., Deep convolutional neural networks for image spam classification,2020

Yu, W., Na, Z., Fengxia, Y. and Yanping, G., Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost. Journal of Integrative Neuroscience, 2018, Volume 17, pp.331-336.
https://doi.org/10.31083/j.jin.2018.04.0410

Smith, M.R.; Martinez, T.R.; Giraud-Carrier, C.G. An instance level analysis of data complexity. Mach. Learn. 2014, 95, 225-256.
https://doi.org/10.1007/s10994-013-5422-z

He, H., Bai, Y., Garcia, E. A. and Li, S. (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning, 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), IEEE, pp. 1322-1328.

J. Kennedy and R. Eberhart, Particle swarm optimization, in Proc. IEEE ICNN Int. Conf. Neural Netw., vol. 4, Nov./Dec. 1995, pp. 1942-1948.

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys., vol. 21, no. 6, pp. 1087-1092, Jun. 1953.
https://doi.org/10.1063/1.1699114

M. Dorigo, M. Birattari, and T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28-39, Nov. 2006.
https://doi.org/10.1109/CI-M.2006.248054

Mirjalili, S., 2019. Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43-55). Springer, Cham.
https://doi.org/10.1007/978-3-319-93025-1_4

Chen, J., Zhao, F., Sun, Y. and Yin, Y., 2020. Improved XGBoost model based on genetic algorithm. International Journal of Computer Applications in Technology, 62(3), pp.240-245.
https://doi.org/10.1504/IJCAT.2020.106571

H. Salmani, M. Tehranipoor, and R. Karri, On design vulnerability analysis and trust benchmarks development, in 2013 IEEE 31st international conference on computer design (ICCD). IEEE, 2013, pp.471-474.
https://doi.org/10.1109/ICCD.2013.6657085

S.-l. D. Scikit, Scikit-learn: Machine learning in python, Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

Bani Younes, A., Batayneh, W., Khamis, A., Cooperative Aerial-Ground Robotic System Using Genetic Algorithm Auto-Tuned Fractional Order PID Control, (2021) International Review of Automatic Control (IREACO), 14 (6), pp. 348-359.
https://doi.org/10.15866/ireaco.v14i6.21365

Jaber, A., Mohammed, K., Shalash, N., Optimization of Electrical Power Systems Using Hybrid PSO-GA Computational Algorithm: a Review, (2020) International Review of Electrical Engineering (IREE), 15 (6), pp. 502-511.
https://doi.org/10.15866/iree.v15i6.18599


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize