Synopsis
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.
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About the Authors
Dr. 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 received his Ph.D. degree from the National Institute of Technology Patna, Bihar, India. Dr. Kumar has more than 20 years of experience in teaching and is currently working as an Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies (UPES), Dehradun, India. His research interests include cloud computing, IoT, and DNA computing. Dr. Kumar has published several edited books, book chapters, patents and papers in conference proceedings and refereed journals. He has also participated in many international conferences as an organizer and session chair. Dr. Kumar is a senior member of IEEE, ACM, IEEE Computer Society, and a Member IEEE Computational Intelligence Society. Dr. Kumar brings a holistic approach to education, fostering student success through quality instruction and innovative pedagogical methods.
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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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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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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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