Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-03
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Language
en
Pages
8
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IEEE Transactions on Artificial Intelligence, Volume 5, issue 3, pp. 977-984
Abstract
Intrusion detection systems (IDS) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially in healthcare, where sensitive information (patient’s medical history, prescriptions, electronic health records, medical bills/debts, and many other sensitive data points) is open to compromise from adversaries. In the big data era, classical machine learning has been applied to train IDS. However, classical IDS tend to be complex: either using several hidden layers susceptible to over-fitting on training data or using overly complex architectures such as convolutional neural networks (CNNs), long-short term memory systems (LSTMs), and recurrent neural networks (RNNs). This paper explored the combination of principles of quantum mechanics and neural networks to train IDS. A hybrid classical-quantum neural architecture is proposed with a quantum-assisted activation function that successfully captures patterns in the dataset while having less architectural memory footprint than classical solutions. The experimental results are demonstrated on the popular KDD99 dataset while comparing our solution to other classical models.Description
Keywords
Biological neural networks, Computer architecture, Computer security, Intrusion detection, Neurons, Quantum computing, Task analysis
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Citation
Laxminarayana, N, Mishra, N, Tiwari, P, Garg, S, Behera, B K & Farouk, A 2024, 'Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems', IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, 9813378, pp. 977-984. https://doi.org/10.1109/TAI.2022.3187676