{"id":10526,"date":"2025-01-04T05:29:31","date_gmt":"2025-01-04T05:29:31","guid":{"rendered":"https:\/\/icertpublication.com\/?page_id=10526"},"modified":"2025-01-05T18:16:37","modified_gmt":"2025-01-05T18:16:37","slug":"quantum-machine-learning-for-anomaly-detection-in-cyber-security-audits","status":"publish","type":"page","link":"https:\/\/icertpublication.com\/index.php\/shodh-sari-2\/sodh-sari-vol-4-issue-1\/quantum-machine-learning-for-anomaly-detection-in-cyber-security-audits\/","title":{"rendered":"Quantum Machine Learning for Anomaly Detection in Cyber Security Audits"},"content":{"rendered":"\t\t
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Quantum Machine Learning for Anomaly Detection in Cyber Security Audits<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Ganapathy, Venkatasubramanian<\/span><\/p>

Faculty in Auditing Department, Southern India Regional Council of the Institute of Chartered Accountants of India (SIRC of ICAI),<\/span><\/p>

Chennai, Tamil Nadu, Bharat<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Abstract\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing\u2019s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms\u2019 ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.<\/span><\/p>

Keywords: <\/span>Quantum Machine Learning (QML), Cyber Security Audit, Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Quantum Principal Component Analysis (QPCA), Anomaly Detection.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Impact Statement<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This research on Quantum Machine Learning (QML) for anomaly detection in cybersecurity audits addresses critical challenges posed by the growing complexity and scale of cyber threats. By leveraging quantum-enhanced models such as Quantum Principal Component Analysis (QPCA) and Quantum Support Vector Machines (QSVM), the study demonstrates how QML can identify subtle anomalies in high-dimensional data with greater speed and precision than classical methods. The findings highlight QML\u2019s potential to revolutionize threat detection by enabling near-real-time processing of large-scale datasets, such as those encountered in IoT networks and critical infrastructure systems.<\/span><\/p>

\u00a0It emphasizes QML\u2019s role in addressing zero-day attacks, insider threats, and Advanced Persistent Threats (APTs) by capturing complex, non-linear relationships within entangled quantum states. This advancement could significantly reduce the financial, operational, and reputational impacts of cyber incidents. By supporting continuous auditing rather than periodic reviews, QML offers a proactive approach to cybersecurity.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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About The Author<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Mr. Venkatasubramanian Ganapathy, M.Phil., B.Ed., M. Com, D.P.C.S.<\/span> is a faculty in Auditing Department, Southern India Regional Council of the Institute of Chartered Accountants of India (SIRC of ICAI), Chennai, Tamil Nadu, Bharat. He has over 18+ years\u2019 academic experience and 9 years corporate experience. He has presented and published many research papers in International and National Conferences and journals. His area of interest are Auditing, Finance and Accounting, Taxation, AI, ML, DL, Cloud Computing, IoT, Osmotic Computing, Blockchain Technology, Big Data Analytics, Python, RDBMS, Serverless Computing, Forensic Auditing, Cyber Security, Quantum Computing etc., He has been recognized with many Awards. His focus on implementation of latest technologies in his field.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u00a0<\/p>

  1. Blog of the Fraunhofer institute for applied and integrated security AISEC. <\/span>https:\/\/www.cybersecurity.blog.aisec.fraunhofer.de\/en\/<\/span><\/a><\/p><\/li>

  2. Dixit, A. (2024). Navigating the Future: Exploring the strategic integration of artificial intelligence in contemporary management practices. <\/span>Shodh Sari-An International Multidisciplinary Journal<\/span>, <\/span>03<\/span>(02), 295\u2013303. https:\/\/doi.org\/10.59231\/sari7705<\/span><\/p><\/li>

  3. Tiwari, A. K. (2024b). Relevance of innovations in educational research technology of universities. <\/span>Edumania-An International Multidisciplinary Journal<\/span>, <\/span>02<\/span>(01), 235\u2013254. https:\/\/doi.org\/10.59231\/edumania\/9029<\/span><\/p><\/li>

  4. arXiv \u2013 (Cornell University) anomaly detection using QML. <\/span>https:\/\/arxiv.org\/search\/?query=anomaly+detection+using+QML&searchtype=all&source=header<\/span><\/p><\/li>

  5. IBM. Quantum. <\/span>https:\/\/securityintelligence.com\/tag\/quantum-computing\/page\/3\/?mhsrc=ibmsearch_a&mhq=quantum%20computing%20comes%20to%20the%20cloud&_gl=1%2Ai68vs0%2A_ga%2AODk1MDE4MzYyLjE3MzAxOTczMjA.%2A_ga_FYECCCS21D%2AMTczMDE5NzMxOS4xLjEuMTczMDE5NzQ2Mi4wLjAuMA<\/span><\/p><\/li>

  6. International Information and Engineering Technology Association. <\/span>https:\/\/www.iieta.org\/<\/span><\/p><\/li>

  7. NSF (National Science Foundation) public access repository (NSF-PAR). <\/span>https:\/\/par.nsf.gov\/contact<\/span><\/p><\/li><\/ol><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    Quantum Machine Learning for Anomaly Detection in Cyber Security Audits Ganapathy, Venkatasubramanian Faculty in Auditing Department, Southern India Regional Council of the Institute of Chartered Accountants of India (SIRC of ICAI), Chennai, Tamil Nadu, Bharat Abstract Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":10106,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-10526","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/pages\/10526","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/comments?post=10526"}],"version-history":[{"count":19,"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/pages\/10526\/revisions"}],"predecessor-version":[{"id":10635,"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/pages\/10526\/revisions\/10635"}],"up":[{"embeddable":true,"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/pages\/10106"}],"wp:attachment":[{"href":"https:\/\/icertpublication.com\/index.php\/wp-json\/wp\/v2\/media?parent=10526"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}