[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117589-en":3,"doc-seo-117589-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117589,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Quantum Machine Learning for Enhanced Cybersecurity - Proposing a Hypothetical Framework for Next-Generation Security Solutions","Rapid evolution of cyber threats has made conventional security approaches insufficient for managing increasingly sophisticated risks. The study presents a Quantum Machine Learning Cybersecurity Framework that combines quantum computing and machine learning to strengthen protection across multiple dimensions. It integrates Quantum Key Distribution for secure key exchange, Quantum Neural Networks and Quantum Support Vector Machines for anomaly detection, and Quantum Reinforcement Learning for autonomous incident response. A Quantum Authentication module secures identity verification using biometric and behavioral data, complemented by a policy compliance interface to support regulatory adherence. Reported experiments show 96% threat-detection accuracy, 28% faster response time, and 96% success in compliance simulations, supporting adaptive and scalable defense.","2024, 4(1), 32222 e-ISSN: 2184-7665  \nJournal of Technologies  \nInformation and Communication Research Article  \nOPEN ACCESS  \n\n| Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation\u003Cbr>Security Solutions\u003Cbr>Forhad Hossain 1 * , Kamrul Hasan 2 , Al Amin 1 , Shakik Mahmud 3 \u003Cbr>1 St. Francis College, Brooklyn, New York, United States\u003Cbr>2 Trine University, Indiana, United States\u003Cbr>3 College Para, Jaldhaka-5330, Nilphamari, Bangladesh\u003Cbr>* Corresponding Author: [forhad6951@gmail.com](forhad6951@gmail.com)\u003Cbr>Citation: Hossain, F., Hasan, K., Amin, A., & Mahmud, S. (2024). Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication, 4(1), 32222. [https://doi.org/10.55267/rtic/15824](https://doi.org/10.55267/rtic/15824) |  |\n| --- | --- |\n| ARTICLE INFO\u003Cbr>Received: 13 Nov 2024\u003Cbr>Accepted: 30 Dec 2024 | ABSTRACT\u003Cbr>The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and adversarial threat management. The framework also incorporates Quantum Reinforcement Learning (QRL) for autonomous incident response, a Quantum Authentication module for securing identity verification using biometric and behavioral data, and a Policy Compliance Interface powered by Quantum Compliance Analyzers for regulatory adherence. Experimental results demonstrated substantial improvements in cybersecurity metrics, including a 96% accuracy in threat detection, a 28% reduction in incident response time, and a 96% success rate in compliance simulations. These findings underscore the framework's capacity to offer adaptive, scalable, and efficient cybersecurity solutions tailored to modern challenges. This study provides a significant step toward integrating quantum technologies into practical cybersecurity applications, paving the way for future innovations in intelligent, secure, and adaptable defense systems.\u003Cbr>Keywords: Quantum Machine Learning, Cybersecurity, Quantum Neural Networks, Cyber Threats, Quantum Key Distribution |\n\nINTRODUCTION  \nThe rapid advancements in quantum computing hold transformative potential across numerous fields, from material science to optimization and cryptography. However, one of the most significant areas where quantum computing's disruptive capabilities can be observed is in cybersecurity. Quantum computing promises to process information at unprecedented speeds, posing a dual threat and opportunity for data security. On one hand, the quantum capability to solve complex problems such as factorization threatens to break widely used cryptographic protocols like RSA and ECC, which form the foundation of contemporary cybersecurity (Raheman, 2022; Rangan et al., 2022). On the other hand, integrating Quantum Machine Learning (QML) offers groundbreaking techniques to detect, prevent, and adapt to evolving cyber threats. QML's ability to handle  \nmassive datasets and detect complex patterns in quantum-enhanced space provides promising solutions to address the next generation of cybersecurity threats (Mehmood et al., 2024) .  \nTraditional cybersecurity approaches, reliant on classical computing, struggle to meet the demands of modern cyber threats, which are increasingly sophisticated and unpredictable (Balantrapu, 2024) . Classical machine learning algorithms have been leveraged for anomaly detection, intrusion prevention, and mal","cbCaiaVybKWtvwzg","https://ap.wps.com/l/cbCaiaVybKWtvwzg","pdf",775410,1,13,"English","en",105,"# Abstract\n# Introduction\n## Quantum computing opportunities and risks in cybersecurity\n## Limitations of classical cybersecurity approaches\n## Quantum machine learning for threat detection and prediction\n## Quantum key distribution and quantum-resilient defenses\n## Challenges of integrating QML and proposed solutions","[{\"question\":\"What framework does the study propose for next-generation cybersecurity?\",\"answer\":\"It proposes a Quantum Machine Learning Cybersecurity Framework combining quantum computing and machine learning to improve cybersecurity across key functions such as detection, response, authentication, and compliance support.\"},{\"question\":\"How does the framework handle secure communication and identity verification?\",\"answer\":\"It uses Quantum Key Distribution for secure key exchange and a Quantum Authentication module that verifies identity using biometric and behavioral data.\"},{\"question\":\"Which technologies are used for detection, and what results were reported?\",\"answer\":\"Quantum Neural Networks and Quantum Support Vector Machines support anomaly detection and adversarial threat management. Experiments reported 96% accuracy in threat detection, a 28% reduction in incident response time, and 96% success in compliance simulations.\"}]","Quantum Machine Learning for Enhanced Cybersecurity - Proposing a Hypothetical Framework for Next-Generation Security Solutions | PDF",1785677121,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-machine-learning-for-enhanced-cybersecurity-proposing-a-hypothetical-framework-for-next-generation-security-solutions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantum-machine-learning-for-enhanced-cybersecurity-proposing-a-hypothetical-framework-for-next-generation-security-solutions/117589/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What framework does the study propose for next-generation cybersecurity?","Question",{"text":75,"@type":76},"It proposes a Quantum Machine Learning Cybersecurity Framework combining quantum computing and machine learning to improve cybersecurity across key functions such as detection, response, authentication, and compliance support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework handle secure communication and identity verification?",{"text":80,"@type":76},"It uses Quantum Key Distribution for secure key exchange and a Quantum Authentication module that verifies identity using biometric and behavioral data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which technologies are used for detection, and what results were reported?",{"text":84,"@type":76},"Quantum Neural Networks and Quantum Support Vector Machines support anomaly detection and adversarial threat management. 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