As cyber threats grow in scale, sophistication, and frequency, traditional detection methods struggle to keep pace. To address this landscape, researchers and organizations turn to emerging technologies like quantum computing and edge computing. Quantum computing offers increased processing power, capable of analyzing complex data patterns and encryptions. Meanwhile, edge computing enables real-time threat detection and increases response times. By combining these two technologies, it creates smarter, faster, and more adaptive cybersecurity systems. Further exploration into how the convergence of quantum and edge computing can revolutionize cyber threat detection may pave the way for more resilient defense mechanisms in the digital age. Advancing Cyber Threat Detection Through Quantum and Edge Computing explores how quantum computing and artificial intelligence (AI) reshape the landscape of real-time anomaly detection, predictive analytics, and next-gen cybersecurity. It examines how quantum-enhanced AI models can detect patterns, adapt to emerging threats, and revolutionize security frameworks across industries, from finance and healthcare to national security and cloud infrastructure. This book covers topics such as blockchain, threat intelligence, and neural networks, and is a useful resource for computer engineers, security professionals, academicians, researchers, and data scientists.
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Shenson Joseph is a distinguished AI researcher and data science expert. With expertise in Data Science, Analytics, and Artificial Intelligence, he has authored 2 books and authored more than 6 research papers. Shenson has judged many national and international events and actively contributes to editorial boards and conferences. He has earned a master’s degree in data science and a second master’s degree in electrical & computer engineering. He is IEEE senior member and associated with ACM and AAAI (association for the advancement of artificial intelligence).
Dr. C Kishor Kumar Reddy , currently working as Associate Professor, Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India. He has research and teaching experience of more than 12 years. He has published more than 90 research papers in National and International Conferences, Book Chapters, and Journals indexed by Scopus and others. He is an author for 2 text books and 15 edited books. He is the member of ISTE, CSI, IAENG, UACEE, IACSIT
Asegul Hulus serves as an assistant professor while also being recognised as a fellow of the Higher Education Academy (FHEA). She is a distinguished researcher and author specialising in the field of computing. She holds memberships in esteemed organisations, including the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), among others. Additionally, she possesses skills in graphic design, video editing, audio production, and video game proficiency. Dr Hulus serves as a chair, chief editor, and peer reviewer for numerous journals, conferences, and publication houses. Furthermore, she holds a prominent position as one of the founding leaders in her field. Finally, she serves as a mentor for the Women4Cyber Foundation and holds various leadership positions in professional computing committees, such as the Association for Computing Machinery's Council on Women in Computing (ACM-W).
Prof. Dr. Tatjana Sibalija (BSc, MSc, PhD in Mechanical Engineering, from Faculty of Mechanical Engineering, University of Belgrade) is Full Professor at the Faculty of Computing, Union University, Belgrade. Her research interest includes artificial intelligence, data science, manufacturing and industrial engineering, process modelling, design and optimisation. She is the editor of 3 books; author of 6 books, 12 book chapters, 130+ papers published in peer-reviewed international journals / presented at refereed international conferences (h-index=20, i10-index=30), including several invited keynote speeches. She has served as a chair of many conference sessions, a member of 50+ scientific program committees of international conferences, a member of the editorial board and guest editor of several international scientific journals. She has been awarded eight awards and honours. Since 2009, she has been appointed FP7 / Horizon 2020 / Horizon Europe (EU Research Framework Programmes) expert / advisor on the projects review, selection and progress monitoring
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Hardcover. Condition: new. Hardcover. As cyber threats grow in scale, sophistication, and frequency, traditional detection methods struggle to keep pace. To address this landscape, researchers and organizations turn to emerging technologies like quantum computing and edge computing. Quantum computing offers increased processing power, capable of analyzing complex data patterns and encryptions. Meanwhile, edge computing enables real-time threat detection and increases response times. By combining these two technologies, it creates smarter, faster, and more adaptive cybersecurity systems. Further exploration into how the convergence of quantum and edge computing can revolutionize cyber threat detection may pave the way for more resilient defense mechanisms in the digital age. Advancing Cyber Threat Detection Through Quantum and Edge Computing explores how quantum computing and artificial intelligence (AI) reshape the landscape of real-time anomaly detection, predictive analytics, and next-gen cybersecurity. It examines how quantum-enhanced AI models can detect patterns, adapt to emerging threats, and revolutionize security frameworks across industries, from finance and healthcare to national security and cloud infrastructure. This book covers topics such as blockchain, threat intelligence, and neural networks, and is a useful resource for computer engineers, security professionals, academicians, researchers, and data scientists. "This book is your ultimate guide to the future of AI-driven cybersecurity and predictive intelligence. It unpacks how Quantum Computing and AI are revolutionizing fraud detection, cyber defense, and real-time anomaly detection across industries"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798337335513
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Buch. Condition: Neu. Advancing Cyber Threat Detection Through Quantum and Edge Computing | Shenson Joseph (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798337335513 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133815441
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As cyber threats grow in scale, sophistication, and frequency, traditional detection methods struggle to keep pace. To address this landscape, researchers and organizations turn to emerging technologies like quantum computing and edge computing. Quantum computing offers increased processing power, capable of analyzing complex data patterns and encryptions. Meanwhile, edge computing enables real-time threat detection and increases response times. By combining these two technologies, it creates smarter, faster, and more adaptive cybersecurity systems. Further exploration into how the convergence of quantum and edge computing can revolutionize cyber threat detection may pave the way for more resilient defense mechanisms in the digital age. Advancing Cyber Threat Detection Through Quantum and Edge Computing explores how quantum computing and artificial intelligence (AI) reshape the landscape of real-time anomaly detection, predictive analytics, and next-gen cybersecurity. It examines how quantum-enhanced AI models can detect patterns, adapt to emerging threats, and revolutionize security frameworks across industries, from finance and healthcare to national security and cloud infrastructure. This book covers topics such as blockchain, threat intelligence, and neural networks, and is a useful resource for computer engineers, security professionals, academicians, researchers, and data scientists. Seller Inventory # 9798337335513