Cyber-Physical System for Industrial Automation Using Quantum Deep Learning

Rajawat, Anand Singh; Goyal, S. B.; Bedi, Pradeep; Constantin, Neagu Bogdan; Raboacă, Maria Simona; Verma, Chaman

doi:10.1109/smart55829.2022.10047730

Cited by 4 publications

(2 citation statements)

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“…This holds true whether or not one adopts a quantum paradigm. Quantum amplitude embedding and similar techniques are used to convert conventional [13] data into a quantum format compatible with the QRBM. Although QRBMs have a lot of potential, it is vital to remember that their evolution toward large-scale, practically useful quantum computers is still in its infancy.…”

Section: Qrbm Trainingmentioning

confidence: 99%

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Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Rajawat,

Goyal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

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Section: Qrbm Trainingmentioning

confidence: 99%

“…This is typically accomplished through the use of bogus websites or apps that are designed to look like authentic Ethereum platforms. Classification of the QRBM: QRBM is able to go further than standard methods of phishing detection [17] because it analyses the quantum states of data interactions.…”

Section: Phishing Attacksmentioning

confidence: 99%