Hardware trojan detection based on side-channel analysis using power traces and machine learning

• Autorzy: Hoang Van-Phuc

Identyfikatory

DOI

10.15439/2021R26

• Konferencja: Sixth International Conference on Research in Intelligent and Computing
• Języki publikacji: EN

Abstrakty

EN

With the continuous development of the Integrated Circuit (IC) manufacturing where international outsourcing is one of the main trends, hardware Trojan (HT) has been considered as a serious problem for hardware security in modern electronic systems. This paper presents a novel HT detection method based on the side-channel analysis with power traces and the machine learning (ML) technique. Side-channel information of the AES encryption core was acquired by the power consumption measurement equipment and then classified with Softmax regression. The ML technique was applied to classify and detect the HT. The experimental results have clarified the efficiency of the proposed method.

Słowa kluczowe

EN

hardware trojan; power traces; side-channel nalysis; SCA; machine learning

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 27
• Strony: 53--56
• Opis fizyczny: Bibliogr. 9 poz., fot., rys., tab., wykr.

Twórcy

autor

Hoang Van-Phuc

• Le Quy Don Technical University, 236 Hoang Quoc Viet Str., Hanoi, Vietnam

Bibliografia

• 1. W. Ou, J. Zeng, Z. Guo, W. Yan, D. Liu, S. Fuentes, “A Homomorphic-encryption-based Vertical Federated Learning Scheme for Rick Management,” Computer Science and Information Systems, vol. 17, no. 3,pp. 819-834, 2020.
• 2. Faisal Alotaibi, Alexei Lisitsa, “Matrix profile for DDoS attacks detection,” Proceedings of the 16th Conference on Computer Science and Intelligence Systems, Annals of Computer Science and Information Systems, vol. 25, pp. 357-361, 2021.
• 3. Zhifeng Hu, Feng Zhao, Lina Qin, Hongkai Lin, “Network Virus and Computer Network Security Detection Technology Optimization,” Scalable Computing: Practice and Experience, vol. 22. no. 2, 2021.
• 4. S. Bhunia and M. M. Tehranipoor, The Hardware Trojan War: Attacks, Myths, and Defenses, Springer, pp. 15-51, 2018.
• 5. A. Amelian and S.E. Borujeni, “A Side-Channel Analysis for Hardware Trojan detection based on Path Delay Measurement,” Journal of Circuits, Systems, and Computers Vol. 27, No. 9, (2018).
• 6. Elnaggar, R. & Chakrabarty, “Machine Learning for Hardware Security-Opportunities and Risks,” K. J Electron Test (2018) 34: 183.
• 7. N.-T. Do, V.-P. Hoang and V.-S. Doan, “Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks,” 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Ha Long, Vietnam, 2020, pp. 66-69.
• 8. M. Dao, V. Hoang, V. Dao and X. Tran, “An Energy Efficient AES Encryption Core for Hardware Security Implementation in IoT Systems,” Proc. 2018 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam, 2018, pp. 301-304.
• 9. Trojan Benchmarks, Available: https://www.trust-hub.org/resource/benchmarks.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).