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Next Generation Email Security: AI Based Spam Detection
Next Generation Email Security: AI Based Spam Detection
Knygos.lt klubas Knygos.lt nariams
124,24 €
-30%
Įprastai
177,49 €
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This book presents AI-driven approaches for combating spam, phishing, and malware in email systems. It reviews classical techniques, identifies their limitations, and introduces two novel frameworks-FLIDA and G-SFO with adaptive capsule networks. It also highlights the role of quantum machine learning in enabling scalable and resilient email security.Key Features: Concise review of classical and AI-based spam detection. Innovative frameworks: FLIDA and G-SFO-ACapsNet. Insights into quantum para…
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 142
  • ISBN-10: 8743810373
  • ISBN-13: 9788743810377
  • Kalba: Anglų

Next Generation Email Security: AI Based Spam Detection (el. knyga) (skaityta knyga) | knygos.lt

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This book presents AI-driven approaches for combating spam, phishing, and malware in email systems. It reviews classical techniques, identifies their limitations, and introduces two novel frameworks-FLIDA and G-SFO with adaptive capsule networks. It also highlights the role of quantum machine learning in enabling scalable and resilient email security.

Key Features:

  • Concise review of classical and AI-based spam detection.
  • Innovative frameworks: FLIDA and G-SFO-ACapsNet.
  • Insights into quantum paradigms for cybersecurity.
  • Empirical evaluations and comparative performance analyses.
  • Open research issues and future perspectives.

This is a valuable resource for researchers, postgraduate students, and professionals in cybersecurity, AI, and data science, as well as industry practitioners and policymakers working on secure communication technologies.

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  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 142
  • ISBN-10: 8743810373
  • ISBN-13: 9788743810377
  • Kalba: Anglų

This book presents AI-driven approaches for combating spam, phishing, and malware in email systems. It reviews classical techniques, identifies their limitations, and introduces two novel frameworks-FLIDA and G-SFO with adaptive capsule networks. It also highlights the role of quantum machine learning in enabling scalable and resilient email security.

Key Features:

  • Concise review of classical and AI-based spam detection.
  • Innovative frameworks: FLIDA and G-SFO-ACapsNet.
  • Insights into quantum paradigms for cybersecurity.
  • Empirical evaluations and comparative performance analyses.
  • Open research issues and future perspectives.

This is a valuable resource for researchers, postgraduate students, and professionals in cybersecurity, AI, and data science, as well as industry practitioners and policymakers working on secure communication technologies.

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