NG / Pan / Xiang Honeypot Frameworks and Their Applications: A New Framework

1. Auflage 2018
ISBN: 978-981-10-7739-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 81 Seiten, eBook

Reihe: Computer Science (R0)

This book presents the latest research on honeypots and their applications. After introducing readers to the basic concepts of honeypots and common types, it reviews various honeypot frameworks such as web-server-based, client-based, shadow and artificially intelligent honeypots. In addition, it offers extensive information on the contribution of honeypots in some of the most popular malware research area such as DDoS, Worm, APT, forensics and Bot attacks.

The book subsequently tackles the issue of honeypot countermeasures, shows many of the tricks often used by hackers to discover honeypots, and proposes a counter-countermeasure to help conceal them. It then puts forward a new framework that integrates various novel concepts, and which can feasibly be used for the detection of potential ransomware and bitcoin. As such, the book provides non-experts with a concise guide to honeypots, and will also benefit practitioners working on security systems.

Zielgruppe

Research

Autoren/Hrsg.

NG, Chee Keong

Pan, Lei

Xiang, Yang

Inhaltsverzeichnis

1;Preface;7
2;Acknowledgements;8
3;Contents;9
4;1 Introduction to Honeypot;11
4.1;1.1 Introduction;11
4.2;References;13
5;2 Design Honeypots;16
5.1;2.1 The Concept of Honeypot (Basic Taxonomy);16
5.2;2.2 Advanced Taxonomy of Honeypot;19
5.3;2.3 Roadmap of the Honeypot Concept;20
5.4;2.4 Challenges in Designing Honeypot;20
5.5;References;22
6;3 Specialized Honeypot Applications;23
6.1;3.1 Web-Server Based Honeypot;23
6.2;3.2 Web Client-Based Honeypot;26
6.3;3.3 Worm Detection Honeypot;29
6.4;3.4 Bot Detection Honeypot;33
6.5;3.5 Honeytoken;37
6.5.1;3.5.1 Anti-phishing Honeypot;37
6.5.2;3.5.2 Insider Detection Honeypot;40
6.6;3.6 Advanced Persistent Threat Honeypot;44
6.7;References;47
7;4 General Purposed Honeypot Applications;50
7.1;4.1 Dynamic Honeypot;50
7.2;4.2 Artificial Intelligent Honeypot;54
7.3;4.3 Shadow Honeypot;55
7.4;References;57
8;5 Other Honeypot Applications;58
8.1;5.1 Concealment of Honeypot;58
8.2;5.2 Application of Forensic in Honeypot;60
8.2.1;5.2.1 Honeypot Forensic for General Type Attack;62
8.2.2;5.2.2 Honeypot Forensics for Botnet;69
8.3;5.3 Direct Role of Honeypot in a Security System;71
8.4;References;72
9;6 Honeypot Framework, Limitation and Counter-Measure;74
9.1;6.1 Conceptual Framework;74
9.2;6.2 Common Features of Honeypot;76
9.3;6.3 Other Honeypot Detection Method;78
9.4;References;80
10;7 Ramsonware and Honeypot;82
10.1;7.1 Ransomware;82
10.2;7.2 Ransomware Honeypot;83
10.3;7.3 Bitcoin Honeytoken;84
10.4;References;84
11;8 Conclusions and Future Work;86
11.1;8.1 Conclusion;86
11.1.1;8.1.1 Summary;86
11.1.2;8.1.2 Future Research Work;88

Über Autor(innen)

Chee Keong NG completed his Master of IT at Deakin University, Australia. He is now pursuing his PHD in Philosophy (IT). His research interests include honeypots, ransomware and deep learning.

Dr Lei Pan received his Ph.D. in Computer Forensics from Deakin University, Melbourne, Australia, in 2008. He currently teaches at Deakin University, Burwood, Victoria, Australia, where he offers courses on cybersecurity. His research interests include cybersecurity and privacy, software security testing, and the applications of analytics in security and privacy. He is also an active cybersecurity educator on futurelearn.com.

Professor Yang Xiang received his PhD in Computer Science from Deakin University, Australia and is currently the Dean of the Digital Research & Innovation Capability Platform at Swinburne University of Technology, Australia. His research interests include network and system security, data analytics, distributed systems, and networking. Further, he is a Senior Member of the IEEE.