Implementation of a hardware trojan chip detector model using arduino microcontroller

• Autorzy: Abdulsalam Kadeejah , Adebisi John , Durojaiye Victor

Identyfikatory

DOI

10.23743/acs-2021-26

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

hardware trojans; chips; logic test; machine learning; microcontroller

PL

trojan sprzętowy; test logiczny; nauczanie maszynowe; mikrokontroler

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2021
• Tom: Vol. 17, no 4
• Strony: 20--33
• Opis fizyczny: Bibliogr. 14 poz., fig., tab.

Twórcy

autor

Abdulsalam Kadeejah

• University of Lagos, Electrical Electronics and Computer Engineering Department, Nigeria

• https://orcid.org/0000-0001-7856-4269

autor

Adebisi John

• University of Lagos, Electrical Electronics and Computer Engineering Department, Nigeria

• https://orcid.org/0000-0002-1105-7219

autor

Durojaiye Victor

• University of Lagos, Electrical Electronics and Computer Engineering Department, Nigeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).