Intro
Preface
Acknowledgments
Contents
About the Editors
Part I Internet of Things Applications Security
Ephemeral Elliptic Curve Diffie-Hellman to Secure Data Exchange in Internet of Medical Things
1 Introduction
2 Related Work
3 Proposed Approach
4 Experimental Results
5 Conclusion
References
End-to-End Security for IoT Communications: A Practical Implementation
1 Introduction
2 Security Issues in IoT Devices
3 Cloud-Based IoT Architecture
4 The Plug-Pair-Play (P3) Model to Establish a Secure Communication Channel

4.1 Secure Communication Between User and Gateway
4.2 Secure Communication Between Device and Gateway
4.3 Setting Up Shared Key for Owner
4.4 Setting Up Shared Key for Delegate
4.5 Secure Communication Between User and Device
5 Using P3 Connection Model to Update Device Firmware
6 Model Evaluation
6.1 Data Security
6.2 Memory Utilization
7 Conclusion
References
A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks
1 Introduction
2 Background
2.1 Transfer Learning
2.2 Maximum Mean Discrepancy (M2D)
2.3 AutoEncoder

3 Related Work
4 Proposed Deep Transfer Learning Model
4.1 System Structure
4.2 Multi-Maximum Mean Discrepancy AutoEncoder
4.3 Training and Predicting Process Using M2DA
4.3.1 Training Process
4.3.2 Predicting Process
5 Experiment Description
5.1 Bot-IoT Datasets (IoT Datasets)
5.2 Evaluation Metric
5.3 Experimental Setting
5.3.1 Hyper-Parameter Setting
5.3.2 Experimental Set
6 Result and Discussion
6.1 Effectiveness of Transferring Task
6.2 Accuracy Comparison
6.3 Complexity
7 Conclusion
References

Part II Internet, Network and Cloud Applications Security
An Approach to Guide Users Towards Less Revealing InternetBrowsers
1 Introduction
2 Preliminaries
2.1 HTTP
2.2 User-Agent Request Header
2.3 CVE
2.4 NVD
2.5 CVSS
3 Methodology
3.1 The Relative Score
3.2 User Study
3.3 Survey Design
3.4 Results
3.5 Discussion
3.6 CVSS Score
3.7 Final Exposure Score
4 Data Set
4.1 Summary of the Data Set
5 Implementation
6 Related Works
7 Conclusion and Future Work
Appendix
A Survey Questions
A.1 Demografic Questions

A.2 Question Set 1
A.3 Question Set 2
A.4 Question Set 3
A.5 Question Set 4
References
Analysing the Threat Landscape Inside the Dark Web
1 Introduction
2 Literature Review
2.1 The Deep Web
2.2 The Dark Web
2.3 Attacks on TOR
2.3.1 Eclipse Attacks Against TOR Hidden Services
2.3.2 Website Fingerprinting Attack
2.3.3 RAPTOR (Routing Attacks on Privacy in TOR)
2.4 The Dark Web Activity Detection Methods
2.4.1 MLP (Machine Learning Perceptron)
2.4.2 Hadoop-Based Dark Web Threat Intelligence Analysis Framework
2.4.3 Black Widow