Handbook of AI-Driven Threat Detection and Prevention

A Holistic Approach to Security
1. Auflage 2025
ISBN: 978-1-032-85974-3
Verlag: Taylor & Francis Ltd

Buch, Englisch, 388 Seiten, Format (B × H): 156 mm x 234 mm

In today’s digital age, the risks to data and infrastructure have increased in both range and complexity. As a result, companies need to adopt cutting-edge artificial intelligence (AI) solutions to effectively detect and counter potential threats. This handbook fills the existing knowledge gap by bringing together a team of experts to discuss the latest advancements in security systems powered by AI. The handbook offers valuable insights on proactive strategies, threat mitigation techniques, and comprehensive tactics for safeguarding sensitive data.

Handbook of AI-Driven Threat Detection and Prevention: A Holistic Approach to Security explores AI-driven threat detection and prevention, and covers a wide array of topics such as machine learning algorithms, deep learning, natural language processing, and so on. The holistic view offers a deep understanding of the subject matter as it brings together insights and contributions from experts from around the world and various disciplines including computer science, cybersecurity, data science, and ethics. This comprehensive resource provides a well-rounded perspective on the topic and includes real-world applications of AI in threat detection and prevention emphasized through case studies and practical examples that showcase how AI technologies are currently being utilized to enhance security measures. Ethical considerations in AI-driven security are highlighted, addressing important questions related to privacy, bias, and the responsible use of AI in a security context. The investigation of emerging trends and future possibilities in AI-driven security offers insights into the potential impact of technologies like quantum computing and blockchain on threat detection and prevention.

This handbook serves as a valuable resource for security professionals, researchers, policymakers, and individuals interested in understanding the intersection of AI and security. It equips readers with the knowledge and expertise to navigate the complex world of AI-driven threat detection and prevention. This is accomplished by synthesizing current research, insights, and real-world experiences.

Zielgruppe

Professional Reference

Autoren/Hrsg.

Anand, A. Jose

Bhambri, Pankaj

Fachgebiete

• Technische Wissenschaften Technik Allgemein Industrial Engineering
• Mathematik | Informatik Mathematik Operations Research
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Understanding AI and Machine Learning in Security. 2. Data Collection and Preprocessing for Security. 3. Feature Engineering for Threat Detection. 4. Anomaly Detection with Artificial Intelligence. 5. Signature-based Security in Wireless Communication. 6. Behavioral Analysis for Threat Detection. 7. Network Security with Artificial Intelligence. 8. Endpoint Security and Artificial Intelligence in the Financial Sector. 9.  Cloud Security and Artificial Intelligence. 10. Adversarial Attacks on AI Security Systems. 11. Ethical Considerations and Privacy in Artificial Intelligence Powered Security Systems. 12. Artificial Intelligence in Financial Fraud Detection. 13. Graph-based Intelligent Cyber Threat Detection System. 14. Future Trends in Artificial Intelligence Driven Security. 15. Enhancing Cybersecurity with Distributed Models and Sparse Mixture of Experts. 16. Anomaly Detection in SIEM Data: User Behavior Analysis with Artificial Intelligence. 17. AI-Driven Security System for Biometric Surveillance. 18. AI-Powered Predictive Analysis for Proactive Cyber Defense. 19. Deep Learning Techniques for Intrusion Detection in Critical Infrastructure. 20. Quantum Computing and AI Synergies: Strengthening Cybersecurity Resilience. 21. Integrating AI with Blockchain for Decentralized Security and Threat Prevention.

Über Autor(innen)

Dr. Pankaj Bhambri is affiliated with the Department of Information Technology at Guru Nanak Dev Engineering College in Ludhiana. He fulfills the role of the Convener for his Departmental Board of Studies. He possesses nearly two decades of teaching experience. He is an active member of IE India, ISTE New Delhi, IIIE Navi Mumbai, IETE New Delhi, and CSI Mumbai. He has contributed to the various research activities while publishing articles in the renowned SCIE and Scopus journals and conference proceedings. He has also published several international patents. Dr. Bhambri has garnered extensive experience in the realm of academic publishing, having served as an editor/author for a multitude of books in collaboration with esteemed publishing houses. Dr. Bhambri has been honored with several prestigious accolades, including the ISTE Best Teacher Award in 2023 and 2022, the I2OR National Award in 2020, the Green ThinkerZ Top 100 International Distinguished Educators award in 2020, the I2OR Outstanding Educator Award in 2019, the SAA Distinguished Alumni Award in 2012, the CIPS Rashtriya Rattan Award in 2008, the LCHC Best Teacher Award in 2007, and numerous other commendations from various government and nonprofit organizations. He has provided guidance and oversight for numerous research projects and dissertations at the postgraduate and Ph.D. levels. He has successfully organized a diverse range of educational programs, securing financial backing from esteemed institutions such as the AICTE, the TEQIP, among others. Dr. Bhambri’s areas of interest encompass machine learning, bioinformatics, wireless sensor networks, and network security.

Dr. A. Jose Anand is currently associated with Halifax Regional Center for Education, Halifax, Nova Scotia, Canada. Earlier, he had worked as a professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, Tamil Nadu, India. He has one year of industrial experience, 24 years of teaching experience, and one year of experience as an assistant for Halifax Regional Center for Education at Saint Mary’s Elementary School, Halifax, Nova Scotia, Canada. He has presented several papers at national and international conferences. He has published several papers in national and international journals and has published books for polytechnic and engineering subjects. He is a member of CSI, IEI, IET, IETE, ISTE, INS, QCFI, and EWB. His current research interests are wireless sensor networks, embedded systems, Internet of Things, machine learning, and image processing.