Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorLaxminarayana, Nikhilen_US
dc.contributor.authorMishra, Nimishen_US
dc.contributor.authorTiwari, Prayagen_US
dc.contributor.authorGarg, Sahilen_US
dc.contributor.authorBehera, Bikash K.en_US
dc.contributor.authorFarouk, Ahmeden_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Marttinen P.en
dc.contributor.organizationIndian Institute of Information Technology Allahabaden_US
dc.contributor.organizationÉcole de technologie supérieureen_US
dc.contributor.organizationBikash's Quantum (OPC) Private Limiteden_US
dc.contributor.organizationSouth Valley Universityen_US
dc.date.accessioned2022-08-10T08:19:16Z
dc.date.available2022-08-10T08:19:16Z
dc.date.issued2024-03en_US
dc.description.abstractIntrusion 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.en
dc.description.versionPeer revieweden
dc.format.extent8
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLaxminarayana, 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.2022.3187676en
dc.identifier.doi10.1109/TAI.2022.3187676en_US
dc.identifier.issn2691-4581
dc.identifier.otherPURE UUID: 5a82d894-8ef1-47d5-ae53-4f20b10d3a19en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/5a82d894-8ef1-47d5-ae53-4f20b10d3a19en_US
dc.identifier.otherPURE LINK: https://ieeexplore.ieee.org/document/9813378/en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85133762247&partnerID=8YFLogxKen_US
dc.identifier.otherPURE LINK: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9813378en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/85271772/Quantum_Assisted_Activation_for_Supervised.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/115779
dc.identifier.urnURN:NBN:fi:aalto-202208104601
dc.language.isoenen
dc.publisherURSI/IEEE
dc.relation.ispartofseriesIEEE Transactions on Artificial Intelligenceen
dc.relation.ispartofseriesarticlenumber 9813378en
dc.rightsopenAccessen
dc.subject.keywordBiological neural networksen_US
dc.subject.keywordComputer architectureen_US
dc.subject.keywordComputer securityen_US
dc.subject.keywordIntrusion detectionen_US
dc.subject.keywordNeuronsen_US
dc.subject.keywordQuantum computingen_US
dc.subject.keywordTask analysisen_US
dc.titleQuantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systemsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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