As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists.
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Hewa Majeed Zangana is an Assistant Professor currently affiliated with Duhok Polytechnic University (DPU) in Iraq. He holds a Doctorate in Philosophy (PhD) degree in ITM, which he is currently pursuing at DPU. Prior to his current role, Hewa Majeed Zangana has held various academic and managerial positions. He previously served as an Assistant Professor at Ararat Private Technical Institute and a Lecturer at DPU's Amedi Technical Institute, and Nawroz University. He has also held administrative positions such as Curriculum Division Director - Presidency of DPU, Information Unit Manager of The Research Center at Duhok Polytechnic University, Head of the Computer Science Department at Nawroz University, and Acting Dean of the College of Computer and IT at Nawroz University. Hewa Majeed Zangana's research interests span a wide range of topics in computer science, including network systems, information security, mobile communication, data communication, and intelligent systems. He has published extensively in peer-reviewed journals such as IEEE, EAI, IGI-Global, Inform, Indonesian Journal of Education and Social Science, TIJAB, INJIISCOM, and AJNU. In addition to his research contributions, Hewa Majeed Zangana has been actively involved in editorial roles such as Editorial Board Member at Applied and Computational Engineering (ewapublishing.org), serving as a Reviewer for the Scientific Journals of Nawroz University. He has also been a member of various scientific committees and administrative bodies at Nawroz University, including the Scientific Curriculum Development Committee, the Student Follow-up Program Committee, and the Committee for the Preparation of the Rules of Procedure for Consultative Offices.
Marwan Omar received the master's degree in information systems and technology from the University of Phoenix, in 2009, and the Ph.D. degree in digital systems security from Colorado Technical University, in 2012. He is currently an Associate Professor at the Illinois Institute of Technology, where he conducts research as well as teaches cyber security.
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Paperback. Zustand: new. Paperback. As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798337371344
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Paperback. Zustand: new. Paperback. As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9798337371344
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Taschenbuch. Zustand: Neu. Securing Large Language Models Against Emerging Threats | Hewa Majeed Zangana (u. a.) | Taschenbuch | Englisch | 2025 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337371344 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 134269454
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists. Bestandsnummer des Verkäufers 9798337371344
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