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Filters: Author is Priyatharishini, M  [Clear All Filters]
2022-05-19
Sai Sruthi, Ch, Lohitha, M, Sriniketh, S.K, Manassa, D, Srilakshmi, K, Priyatharishini, M.  2021.  Genetic Algorithm based Hardware Trojan Detection. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 1:1431–1436.
There is an increasing concern about possible hostile modification done to ICs, which are used in various critical applications. Such malicious modifications are referred to as Hardware Trojan. A novel procedure to detect these malicious Trojans using Genetic algorithm along with the logical masking technique which masks the Trojan module when embedded is presented in this paper. The circuit features such as transition probability and SCOAP are used as suitable parameters to identify the rare nodes which are more susceptible for Trojan insertion. A set of test patterns called optimal test patterns are generated using Genetic algorithm to claim that these test vectors are more feasible to detect the presence of Trojan in the circuit under test. The proposed methodologies are validated in accordance with ISCAS '85 and ISCAS '89 benchmark circuits. The experimental results proven that it achieves maximum Trigger coverage, Trojan coverage and is also able to successfully mask the inserted Trojan when it is triggered by the optimal test patterns.
2021-05-26
Gayatri, R, Gayatri, Yendamury, Mitra, CP, Mekala, S, Priyatharishini, M.  2020.  System Level Hardware Trojan Detection Using Side-Channel Power Analysis and Machine Learning. 2020 5th International Conference on Communication and Electronics Systems (ICCES). :650—654.

Cyber physical systems (CPS) is a dominant technology in today's world due to its vast variety of applications. But in recent times, the alarmingly increasing breach of privacy and security in CPS is a matter of grave concern. Security and trust of CPS has become the need of the hour. Hardware Trojans are one such a malicious attack which compromises on the security of the CPS by changing its functionality or denial of services or leaking important information. This paper proposes the detection of Hardware Trojans at the system level in AES-256 decryption algorithm implemented in Atmel XMega Controller (Target Board) using a combination of side-channel power analysis and machine learning. Power analysis is done with help of ChipWhisperer-Lite board. The power traces of the golden algorithm (Hardware Trojan free) and Hardware Trojan infected algorithms are obtained and used to train the machine learning model using the 80/20 rule. The proposed machine learning model obtained an accuracy of 97%-100% for all the Trojans inserted.