Hardware Security in Sensor and its Networks

Monjur, Mohammad Mezanur Rahman; Heacock, Joseph; Calzadillas, Joshua; Mahmud, Md Shaad; Roth, John R.; Mankodiya, Kunal; Sazonov, Edward; Yu, Qiaoyan

doi:10.3389/fsens.2022.850056

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…The prediction of possible noisy trend is important also to find possible hardware analog Trojans [33][34][35].…”

Section: Intentional Manumissionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

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“…The prediction of possible noisy trend is important also to find possible hardware analog Trojans [33][34][35].…”

Section: Intentional Manumissionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

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“…From the security point of view, the SN begins at sensors. If data reliability shall be guaranteed then all sensors have to be secured from the HW and SW point of view [6]. The HW security is essential in order to prevent any unauthorized change of parts containing the SW, which could possibly compromise the measured data.…”

Section: Environmentmentioning

confidence: 99%

General Sensor Network application approach

Koval

Havlíček²,

Tesař³

2023

Acta IMEKO

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“…The embedding of Hardware Trojans, backdoors, and other malicious circuits within counterfeit integrated circuits (ICs) poses a significant threat, endangering the security, confidentiality, and availability of electronic systems [22]. This underscores the critical need for rigorous hardware security measures, including supply chain oversight, secure manufacturing processes, and state-of-the-art counterfeit detection technologies, to forestall unauthorized access [23], data breaches [24], or system malfunctions [25].…”

Section: Hardware Securitymentioning

confidence: 99%

Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption Challenges

Nyako,

Devkota,

et al. 2023

Electronics

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The field of microelectronics has experienced extensive integration into various aspects of our everyday lives, evident via its utilization across a wide range of devices such as cellphones, airplanes, computers, wristwatches, and other similar technologies. Microelectronics are vital to the healthcare and defense industries, making them vulnerable to counterfeit products. Currently, the complicated global microelectronics supply chain involves the production of varied components in multiple places, resulting in tremendous risk. In this scenario, it is possible for hostile or adversarial actors to exploit the situation by intentionally introducing counterfeit components. This hostile behavior could steal data or use these components as remote kill switches. To address these problems, enormous resources are being committed to research, innovation, and development to build trust in microelectronics. This research study provides a thorough analysis of the taxonomy associated with prominent attack, detection, and avoidance models in the realm of counterfeit microelectronics. This research aims to improve our understanding of dependable microelectronics. Prevention strategies like Physical Unclonable Functions (PUFs) and machine learning (ML), and detection methods like aging-based fingerprints are reviewed in this study. Finally, we underscore the significance of interdisciplinary cooperation, commitment to norms, and proactive methods.

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Hardware Security in Sensor and its Networks

Cited by 10 publications

References 22 publications

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

General Sensor Network application approach

Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption Challenges

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