Exploiting Machine Learning for Robust Security
ISBN 13: 9798369377581
Published by IGI Global, 2025
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In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers.
About the Authors:
Minakshi is an accomplished academician with 18+ years of experience in computer science and engineering domains. Currently an Assistant Professor at King Khalid University, she has taught at esteemed institutions like Uttaranchal University and Tula's Institute. A prolific researcher with 80 Scopus publications, 18 patents, and 3 software copyrights, she has received accolades like Outstanding Section Volunteer Award (IEEE UP Section), Research Excellence Award, and HOD of the Year Award. Adept at curriculum development, research guidance, project management, and team leadership, Dr. Minakshi brings a holistic approach to education, fostering student success through quality instruction and innovative pedagogical methods.
Anchit Bijalwan is an accomplished academician and researcher with a Ph.D. in Computer Science and Engineering. Currently serving as Research Coordinator at the British University Vietnam, he has over 15 years of experience in academia. His research interests include network forensics, cybersecurity, machine learning, and data mining. Dr. Bijalwan has authored two books and published extensively in prestigious journals like Security and Communication Networks, Discrete Dynamics in Nature and Society, and Journal of Healthcare Engineering. He has delivered international training programs, chaired conferences, and served as an examiner and editor for reputed publications. His contributions have been recognized through awards like the International Researcher Award in 2021.
Tarun Kumar is currently working as Assistant Professor in School of Computing Science & Engineering. He has total experience of more than 17 years of teaching and has been associated with Galgotias University since 2017. Along with he is a research scholar in the Department of CSE at the National Institute of Technology Patna, Bihar, India. He is firm Believer of productivity and efficiency in work. He exhibits an honest work ethic with the ability to excel in fast-paced, time-sensitive environment. Being a passionate teacher, he believes that teaching is not merely restricted to making the students understand the underlying concepts of a course but also to develop critical thinking and evaluate alternate approaches for problem solving. He always put his efforts towards the overall developments of his students. His research interest area is Cloud Computing, DNA Computing. He has published several papers in peer reviewed journal and international conferences. He also organized and attended several workshops and conferences.
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Title: Exploiting Machine Learning for Robust ...
Publisher: IGI Global
Publication Date: 2025
Binding: Hardcover
Condition: New
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Paperback. Condition: new. Paperback. In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers. 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 # 9798369377598
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Taschenbuch. Condition: Neu. Exploiting Machine Learning for Robust Security | Minakshi (u. a.) | Taschenbuch | Englisch | 2025 | IGI Global | EAN 9798369377598 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133486806
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Exploiting Machine Learning for Robust Security (Paperback)
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Paperback. Condition: new. Paperback. In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369377598
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Exploiting Machine Learning for Robust Security (Hardcover)
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Hardcover. Condition: new. Hardcover. In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers. 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 # 9798369377581
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Exploiting Machine Learning for Robust Security (Hardcover)
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Hardcover. Condition: new. Hardcover. In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369377581
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Exploiting Machine Learning for Robust Security (Paperback)
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Paperback. Condition: new. Paperback. In the digital world, ensuring robust security is critical as cyber threats become more sophisticated and pervasive. Machine learning can be used to strengthen cybersecurity and offer dynamic solutions that can identify, predict, and mitigate potential risks with unprecedented accuracy. By analyzing vast amounts of data, detecting patterns, and adapting to evolving threats, machine learning enables security systems to autonomously respond to anomalies and protect sensitive information in real-time. As technology advances, the integration of machine learning into security systems represents a critical step towards creating adaptive protection against the complex challenges of modern cybersecurity. Further research into the potential of machine learning in enhancing security protocols may highlight its ability to prevent cyberattacks, detect vulnerabilities, and ensure resilient defenses. Exploiting Machine Learning for Robust Security explores the world of machine learning, discussing the darknet of threat detection and vulnerability assessment, malware analysis, and predictive security analysis. Using case studies, it explores machine learning for threat detection and bolstered online defenses. This book covers topics such as anomaly detection, threat intelligence, and machine learning, and is a useful resource for engineers, security professionals, computer scientists, academicians, and researchers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798369377598
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