Quantum Machine Learning in Intrusion Detection Systems: a Systematic Mapping Study

dc.authorscopusid 57208838488
dc.authorscopusid 16237826800
dc.contributor.author Faker,O.
dc.contributor.author Cagiltay,N.E.
dc.date.accessioned 2024-07-05T15:50:44Z
dc.date.available 2024-07-05T15:50:44Z
dc.date.issued 2024
dc.department Atılım University en_US
dc.department-temp Faker O., Graduate School of Natural and Applied Sciences, Atilim University, Ankara, Turkey; Cagiltay N.E., Graduate School of Natural and Applied Sciences, Atilim University, Ankara, Turkey en_US
dc.description.abstract The integration between quantum computing (QC) and machine learning algorithms (ML) aims to speed up computing processes and increase model accuracy rates, and this is what led developers and researchers to exploit this feature to study improving the performance of intrusion detection systems (IDSs). In this work, we present a systematic mapping review (SMR) of the most important works in the field of using quantum machine learning (QML) to increase the efficiency of anomaly detection techniques, which depend mainly on ML. After defining and applying the research methodology, the preliminary search results amounted to 240 studies from four databases. According to the exclusion and inclusion reports, we obtained 21 main studies. After reviewing and analyzing the results, the four research questions were answered. The review focused on the development of integration of intrusion detection systems with QML, the characteristics of such integration, increasing the efficiency of ML algorithms, and future research opportunities in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1007/978-981-99-7886-1_9
dc.identifier.endpage 113 en_US
dc.identifier.isbn 978-981997885-4
dc.identifier.issn 2367-3370
dc.identifier.scopus 2-s2.0-85192184042
dc.identifier.scopusquality Q4
dc.identifier.startpage 99 en_US
dc.identifier.uri https://doi.org/10.1007/978-981-99-7886-1_9
dc.identifier.uri https://hdl.handle.net/20.500.14411/4181
dc.identifier.volume 817 en_US
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation.ispartof Lecture Notes in Networks and Systems -- 7th World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2023 -- 21 August 2023 through 24 August 2023 -- London -- 309419 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 2
dc.subject Anomaly detection en_US
dc.subject Cyberattack en_US
dc.subject Intrusion detection systems (IDSs) en_US
dc.subject Machine learning (ML) cybersecurity en_US
dc.subject Quantum computing (QC) en_US
dc.subject Quantum machine learning (QML) en_US
dc.title Quantum Machine Learning in Intrusion Detection Systems: a Systematic Mapping Study en_US
dc.type Conference Object en_US
dspace.entity.type Publication

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