Honeypot Classifications

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Table of contents

Introduction 
2.1 Context
2.2 Problem Statement
2.3 Contributions
I State of the art
3 Honeypots 
3.1 Honeypot Evolution
3.2 Honeypot Classifications
3.3 Honeypot Research Activities
3.3.1 Attacker Observation and Information Gathering
3.3.2 Honeypot Management
3.3.3 Distributed Honeypot Operation
3.3.4 Honeypot Data Analysis
3.4 Detecting Honeypots
3.5 Summary
3.6 Limitations
4 Learning in Games 
4.1 Game Theory
4.2 Reinforcement Learning
4.2.1 Markov Decision Process
4.2.2 Learning Agents
4.3 Multi-Agent Learning Founded on Game Theory
4.4 Summary
II Contributions 
5 Modeling Adaptive Honeypots 
5.1 Modeling Attacker Behavior
5.1.1 Hierarchical Probabilistic Automaton
5.1.2 Attacker Responses
5.2 Honeypot Behaviors
5.3 Summary
6 Learning in Honeypot Games 
6.1 Game Theory and High-Interaction Honeypots
6.1.1 Defining Payoffs
6.1.2 Computing Payoffs with Simulations
6.1.3 Leveraging Optimal Strategy Profiles
6.2 Learning Honeypots Operated by Reinforcement Learning
6.2.1 Environment
6.2.2 Honeypot Actions
6.2.3 Rewards
6.2.4 Learning Agents
6.3 Fast Concurrent Learning Honeypot
6.3.1 Attacker and Honeypot Rewards
6.3.2 Learning Honeypot and Attackers
6.4 Summary
6.5 Limitations
7 Honeypot Operation 
7.1 Netflow Analysis
7.2 Network Activity Identification
7.3 Full Network Capture Analysis
7.3.1 Network Forensic Tool Analysis
7.4 User Mode Linux Tests
7.5 In vivo Malware Analysis
7.5.1 Tree- and Graph-based kernels
7.5.2 The Process Tree Model
7.5.3 The Process Graph Model
7.6 Implementation of Adaptive Honeypots
7.6.1 Adaptive Honeypot – Framework
7.6.2 Component Description
7.7 Conclusions
7.8 Limitations
8 Experimental Evaluations 
8.1 Recovering High-Interaction Honeypot Traces
8.2 Recovering Low-Interaction Honeypot Traces
8.3 Computing Nash Equilibria
8.4 Reinforcement Learning Driven Honeypots
8.5 Honeypot Comparison
8.6 Fast Concurrent Learning
8.7 Conclusions
9 Conclusions and Perspectives 
9.1 Summary of the thesis
9.2 Insights
9.3 Limitations
9.3.1 System Attacks
9.3.2 Behavioral attacks
9.4 Future Work
9.4.1 Alternative Honeypot Designs and Feature Extensions
9.4.2 Additional Honeypot – Attacker System Games
A Vulnerability Measurements 
B Quantitative Publication Analysis 
B.1 Trend Analysis
B.2 Publication Measurements
C Honeypot Operation 
C.1 Forensic Tool Exploits
D Experimental Evaluations 
D.1 Modification of the Linux Authentication Modules
D.2 Kernel Modifications
D.3 Message Exchange