Genetic Algorithm based Hardware Trojan Detection

Sruthi, Ch Sai; Lohitha, M; Sriniketh, S.K; Manassa, D; Srilakshmi, K.; Priyatharishini, M.

doi:10.1109/icaccs51430.2021.9441738

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Logic tests [7], [13]- [15] and side-channel analysis [5], [9], [10] are the major anomaly detection methods for IoT devices. Logic tests can detect abnormal functions within a circuit by comparing the actual outputs to the expected ones, and they are also used for post-design verification.…”

Section: Related Workmentioning

confidence: 99%

An Anomalous Behavior Detection Method Utilizing IoT Power Waveform Shapes

Takasaki

Togawa

2024

IEICE Trans. Fundamentals

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In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize consumed energy and operation duration time extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar consumed energy and duration time but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shapebased distance (SBD) measure. The proposed method first obtains the entire power waveform of the target IoT device and extracts several application power waveforms. After that, we give the invariances to them, and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects anomalous application behaviors, while the existing state-of-the-art method fails to detect them.

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