Edge Intelligence

Name: Edge Intelligence
Price: 180 USD
Availability: InStock
ISBN: 9780443382970

Advanced Deep Transfer Learning for IoT Security

by Jawad Ahmad, Shahid Latif, Wadii Boulila, Anis Koubaa, Mujeeb Ur Rehman, Imdad Ullah Khan

1st Edition

Publisher: Syngress

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Book Details

Print ISBN	9780443382970
eText ISBN	9780443382987
Publisher	Syngress
Publishing Year	2026
Edition	1st Edition
Language	English

Edge Intelligence: Advanced Deep Transfer Learning for IoT Security, 1st Edition, addresses the protection of Internet of Things (IoT) and Industrial Internet of Things (IIoT) infrastructure. Written by Jawad Ahmad, Shahid Latif, Wadii Boulila, Anis Koubaa, Mujeeb Ur Rehman, and Imdad Ullah Khan, this work examines how machine learning models support defensive operations across distributed edge networks.

The material links foundational deep learning concepts directly to hardware execution challenges. Early sections outline fundamental neural network architectures and edge computing structures. The text then transitions into threat mitigation, focusing on intrusion detection, anomaly monitoring, secure communication protocols, and privacy-preserving methods suited for embedded environments.

To assist technical implementation, the work details resource-efficient model design alongside practical industry applications. On January 16, 2026, the text received WCAG 2.2 level AA accessibility conformance certification. The publication directly aligns with the operational needs of information security professionals, software developers, forensic analysts, and computer science researchers.

Chapter 1: Introduction to IoT and IIoT Security
- • 1.1. Overview of IoT/IIoT
- • 1.2. Current security
- • 1.3. Basics of Edge Computing
Chapter 2: Fundamentals of Deep Learning and Transfer Learning
- • 2.1. Deep learning concepts
- • 2.2. Transfer learning principles
Chapter 3: Edge Computing: Architecture and Security
- • 3.1. Secure Architecture Design
- • 3.2. Security protocols at the edge
- • 3.3. Case studies of edge security implementations
Chapter 4: Deep Transfer Learning for Intrusion and Anomaly Detection
- • 4.1. Intrusion detection systems (IDS)
- • 4.2. Application of deep transfer learning in IDS
- • 4.3. Anomaly detection using deep transfer learning
Chapter 5: Resource-Efficient Models for Edge Devices
- • 5.1. Challenges and Strategies
- • 5.2. Designing lightweight models
- • 5.3. Optimization techniques for resource-constrained environments
- • 5.4. Real-world applications and case studies
- • 5.5. Adaptation for Edge Devices
Chapter 6: Secure Communication and Privacy-Preserving Techniques in Edge Intelligence
- • 6.1. Secure Communication
- • 6.2. Privacy concerns in IoT/IIoT
- • 6.3. Balancing security and privacy
Chapter 7: Case Studies and Industry Applications
- • 7.1. Analysis of industry-specific applications
- • 7.2. Case Studies of Implementation
- • 7.3. Impact on Business and Society
Chapter 8: Future Trends and Emerging Technologies in IoT Security
- • 8.1. Predictions for the future of IoT/IIoT security
- • 8.2. Emerging technologies in edge intelligence
- • 8.3. Predictions and Upcoming Challenges
- • 8.4. Preparing for the next wave of cybersecurity challenges
Chapter 9: Developing and Implementing a Comprehensive IoT Security Strategy
- • 9.1. Assessment and Risk Management
- • 9.2. Developing Adaptive Security Policies
- • 9.3. Implementing Edge Intelligence in IoT/IIoT
- • 9.4. Importance of Skilling and Training
- • 9.5. Resources and Programs
Chapter 10: Conclusion
- • 10.1. Review of Core Concepts
- • 10.2. Strategic Insights
- • 10.3. Technological Advancements
- • 10.4. Evolving Security Paradigms