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Machine intelligence applications in cyber-risk management

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine intelligence applications in cyber-risk management/ Mohammed Amin Almaiah, Yassine Maleh, editors.
other author:	Maleh, Yassine,
Published:	Hershey, Pennsylvania :IGI Global, : 2025.,
Description:	1 online resource (xix, 492 p.) :ill.
[NT 15003449]:	Preface -- Section 1. Foundations of Cyber-Risk and Emerging Technologies -- Chapter 1. Advancements and Challenges in the Internet of Drones Security Issues: A Comprehensive Review -- Chapter 2. Security Challenges and Solutions inIoT: An In-Depth Review of Anomaly Detection and Intrusion Prevention -- Chapter 3. Analysis of Blackhole Attack in RPL-Based 6LoWPAN Network Using Contiki-NG -- Chapter 4. DIS Flooding Attack Impact in RPL-Based 6LowPAN Network -- Section2. Machine Intelligence for Malware Detection and Cybersecurity -- Chapter 5. Artificial Intelligence, Terrorism, and Cyber Security: Challenges and Opportunities -- Chapter 6. AI-Based Solutions for Malware Detection and Prevention -- Chapter 7. Evolving AI-Based Malware Detection: A Hybrid Approach Combining Transfer Learning and Explainable AI -- Chapter 8. Phishing URL Detection Using BiLSTM With Attention Mechanism -- Section 3. Machine Intelligence and Cyber-Risk Management -- Chapter 9. Cyber Risk Assessment Using Machine Learning Algorithms -- Chapter 10. Explainable AI for Transparent Cyber-Risk Assessment and Decision-Making -- Chapter 11. Application of Information Theoretical Models for AI-Driven Cybersecurity Systems -- Chapter 12. Machine Learning Techniques for Network Security -- Section 4. Cyber-Risk and Anomaly Detection -- Chapter 13. Anomaly Detection Algorithms in Cybersecurity -- Chapter 14. Real-Time Threat Detection in Cybersecurity: Leveraging Machine Learning Algorithms for Enhanced Anomaly Detection -- Chapter 15. Intrusion Detection for Wireless Sensor Networks Using Parrot Algorithm -- Chapter 16. Mining Cybersecurity Intelligence From Spam Emails Using the AI-Based Generative Adversarial Network -- Section 5. Ethical Considerations in Machine Intelligence for Cyber Risk Management -- Chapter 17. Ethical and Legal Implications of AI in Cybersecurity -- Chapter 18. Future Trends and Innovation in Machine Intelligence for Cyber Risk Management -- Compilation of References -- About the Contributors -- Index.
Subject:	Computer security - Technological innovations. -
Online resource:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7540-2
ISBN:	9798369375426

Machine intelligence applications in cyber-risk management
Machine intelligence applications in cyber-risk management[electronic resource] /Mohammed Amin Almaiah, Yassine Maleh, editors. - Hershey, Pennsylvania :IGI Global,2025. - 1 online resource (xix, 492 p.) :ill. - Advances in computational intelligence and robotics (ACIR) book series. - Advances in computational intelligence and robotics (ACIR) book series..

Includes bibliographical references and index.

Preface -- Section 1. Foundations of Cyber-Risk and Emerging Technologies -- Chapter 1. Advancements and Challenges in the Internet of Drones Security Issues: A Comprehensive Review -- Chapter 2. Security Challenges and Solutions inIoT: An In-Depth Review of Anomaly Detection and Intrusion Prevention -- Chapter 3. Analysis of Blackhole Attack in RPL-Based 6LoWPAN Network Using Contiki-NG -- Chapter 4. DIS Flooding Attack Impact in RPL-Based 6LowPAN Network -- Section2. Machine Intelligence for Malware Detection and Cybersecurity -- Chapter 5. Artificial Intelligence, Terrorism, and Cyber Security: Challenges and Opportunities -- Chapter 6. AI-Based Solutions for Malware Detection and Prevention -- Chapter 7. Evolving AI-Based Malware Detection: A Hybrid Approach Combining Transfer Learning and Explainable AI -- Chapter 8. Phishing URL Detection Using BiLSTM With Attention Mechanism -- Section 3. Machine Intelligence and Cyber-Risk Management -- Chapter 9. Cyber Risk Assessment Using Machine Learning Algorithms -- Chapter 10. Explainable AI for Transparent Cyber-Risk Assessment and Decision-Making -- Chapter 11. Application of Information Theoretical Models for AI-Driven Cybersecurity Systems -- Chapter 12. Machine Learning Techniques for Network Security -- Section 4. Cyber-Risk and Anomaly Detection -- Chapter 13. Anomaly Detection Algorithms in Cybersecurity -- Chapter 14. Real-Time Threat Detection in Cybersecurity: Leveraging Machine Learning Algorithms for Enhanced Anomaly Detection -- Chapter 15. Intrusion Detection for Wireless Sensor Networks Using Parrot Algorithm -- Chapter 16. Mining Cybersecurity Intelligence From Spam Emails Using the AI-Based Generative Adversarial Network -- Section 5. Ethical Considerations in Machine Intelligence for Cyber Risk Management -- Chapter 17. Ethical and Legal Implications of AI in Cybersecurity -- Chapter 18. Future Trends and Innovation in Machine Intelligence for Cyber Risk Management -- Compilation of References -- About the Contributors -- Index.

"In an era where cyber threats are increasingly sophisticated and persistent, the intersection of machine intelligence and cyber-risk management represents a pivotal frontier in the defense against malicious actors. The rapid advancements of artificial intelligence (AI) and machine learning (ML) technologies offer unprecedented capabilities for identifying, analyzing, and mitigating cyber risks. These technologies not only improve the speed and accuracy of identifyingpotential threats but also enable proactive and adaptive security measures. Machine Intelligence Applications in Cyber-Risk Management explores the diverse applications of machine intelligence in cyber-risk management, providing a comprehensive overview of how AI and ML algorithms are utilized for automated incident response, threat intelligence gathering, and dynamic security postures. It addresses the pressing need for innovative solutions to combat cyber threats and offerinsights into the future of cybersecurity, where machine intelligence plays a crucial role in creating resilient and adaptive defense mechanisms. Covering topics such as anomy detection algorithms, malware detection, and wireless sensor networks (WSNs), this book is an excellent resource for cybersecurity professionals, researchers, academicians, security analysts, threat intelligence experts, IT managers, and more."--

Mode of access: World Wide Web.

ISBN: 9798369375426Subjects--Topical Terms:

3408488
Computer security
--Technological innovations.Subjects--Index Terms:

Anomaly Detection Algorithms.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA76.9.A25 / M3133 2025eb

Dewey Class. No.: 006.3/1

Machine intelligence applications in cyber-risk management
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245 0 0 $a Machine intelligence applications in cyber-risk management $h [electronic resource] / $c Mohammed Amin Almaiah, Yassine Maleh, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2025.
300 $a 1 online resource (xix, 492 p.) : $b ill.
490 1 $a Advances in computational intelligence and robotics (ACIR) book series
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Section 1. Foundations of Cyber-Risk and Emerging Technologies -- Chapter 1. Advancements and Challenges in the Internet of Drones Security Issues: A Comprehensive Review -- Chapter 2. Security Challenges and Solutions inIoT: An In-Depth Review of Anomaly Detection and Intrusion Prevention -- Chapter 3. Analysis of Blackhole Attack in RPL-Based 6LoWPAN Network Using Contiki-NG -- Chapter 4. DIS Flooding Attack Impact in RPL-Based 6LowPAN Network -- Section2. Machine Intelligence for Malware Detection and Cybersecurity -- Chapter 5. Artificial Intelligence, Terrorism, and Cyber Security: Challenges and Opportunities -- Chapter 6. AI-Based Solutions for Malware Detection and Prevention -- Chapter 7. Evolving AI-Based Malware Detection: A Hybrid Approach Combining Transfer Learning and Explainable AI -- Chapter 8. Phishing URL Detection Using BiLSTM With Attention Mechanism -- Section 3. Machine Intelligence and Cyber-Risk Management -- Chapter 9. Cyber Risk Assessment Using Machine Learning Algorithms -- Chapter 10. Explainable AI for Transparent Cyber-Risk Assessment and Decision-Making -- Chapter 11. Application of Information Theoretical Models for AI-Driven Cybersecurity Systems -- Chapter 12. Machine Learning Techniques for Network Security -- Section 4. Cyber-Risk and Anomaly Detection -- Chapter 13. Anomaly Detection Algorithms in Cybersecurity -- Chapter 14. Real-Time Threat Detection in Cybersecurity: Leveraging Machine Learning Algorithms for Enhanced Anomaly Detection -- Chapter 15. Intrusion Detection for Wireless Sensor Networks Using Parrot Algorithm -- Chapter 16. Mining Cybersecurity Intelligence From Spam Emails Using the AI-Based Generative Adversarial Network -- Section 5. Ethical Considerations in Machine Intelligence for Cyber Risk Management -- Chapter 17. Ethical and Legal Implications of AI in Cybersecurity -- Chapter 18. Future Trends and Innovation in Machine Intelligence for Cyber Risk Management -- Compilation of References -- About the Contributors -- Index.
520 3 $a "In an era where cyber threats are increasingly sophisticated and persistent, the intersection of machine intelligence and cyber-risk management represents a pivotal frontier in the defense against malicious actors. The rapid advancements of artificial intelligence (AI) and machine learning (ML) technologies offer unprecedented capabilities for identifying, analyzing, and mitigating cyber risks. These technologies not only improve the speed and accuracy of identifyingpotential threats but also enable proactive and adaptive security measures. Machine Intelligence Applications in Cyber-Risk Management explores the diverse applications of machine intelligence in cyber-risk management, providing a comprehensive overview of how AI and ML algorithms are utilized for automated incident response, threat intelligence gathering, and dynamic security postures. It addresses the pressing need for innovative solutions to combat cyber threats and offerinsights into the future of cybersecurity, where machine intelligence plays a crucial role in creating resilient and adaptive defense mechanisms. Covering topics such as anomy detection algorithms, malware detection, and wireless sensor networks (WSNs), this book is an excellent resource for cybersecurity professionals, researchers, academicians, security analysts, threat intelligence experts, IT managers, and more."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
650 0 $a Computer security $x Technological innovations. $3 3408488
650 0 $a Computer crimes $x Prevention. $3 819286
650 0 $a Machine learning $x Industrial applications. $3 1361578
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
653 $a Anomaly Detection Algorithms.
653 $a Artificial Intelligence (AI)
653 $a Cyber-Risk Management.
653 $a Cybersecurity.
653 $a Deep Learning.
653 $a Drone Security.
653 $a Internet of Things (IoT)
653 $a Intrusion Detection.
653 $a Machine Intelligence.
653 $a Machine Learning (ML)
653 $a Malware Detection.
653 $a Network Security.
653 $a Terrorism.
653 $a Threat Detection.
653 $a Wireless Sensor Networks (WSNs)
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Maleh, Yassine, $d 1987- $3 3793543
700 1 $a Almaiah, Mohammed Amin, $d 1986- $3 3793544
710 2 $a IGI Global. $3 1361470
776 0 8 $i Print version: $z 9798369375402
830 0 $a Advances in computational intelligence and robotics (ACIR) book series. $3 3311060
856 4 0 $u https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7540-2