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Online anomaly detection for reconfigurable self-X real-time operating systems : a danger theory-inspired approach / Diplom-Informatikerin Katharina Stahl. Paderborn, 2016
Inhalt
Acknowledgements
Abstract
Zusammenfassung
I Introduction
1 Introduction
Motivation
Objective
1.1 Requirements, Restrictions and Challenges
1.2 Basic Idea
Structure of this Thesis
II Foundations
2 Anomaly Detection
2.1 Definition
2.2 Properties and Features
2.3 Architectural Model
2.4 Data Classification
2.5 Anomaly Detection Techniques
2.6 Application Domains
2.7 Summary
3 Artificial Immune Systems
3.1 The Human Immune System
3.2 Artificial Immune Systems
3.3 Self/Nonself Discrimination
3.4 Danger Theory
3.5 Evaluation of Artificial Immune Systems
3.6 Summary
4 Related Work
4.1 System Call-based Anomaly Detection
4.2 AIS-inspired Anomaly Detection
4.3 Danger Theory-based Anomaly Detection
4.4 Discussion and Summary
5 Online Pattern Matching
5.1 Introduction
5.2 Suffix Trees
5.3 Summary
6 ORCOS - Organic Reconfigurable Operating System
6.1 ORCOS Design and Architecture
6.2 Offline Configurability
6.3 Operating System Modules
6.4 Summary
III Online Anomaly Detection
7 Online Anomaly Detection for Reconfigurable Real-Time Systems
7.1 Problem Definition and Feature Requirements
7.2 Anomaly Detection Framework
7.3 Behavior Profiling and Knowledge Base
7.4 Architectural Model
7.5 Summary
IV Implementation and Evaluation
8 ORCOS Online Anomaly Detection Framework
8.1 Online Reconfigurability
8.2 Basic Self-X Architecture
8.3 Architecture for Anomaly Detection
8.4 System Call Monitor
8.5 Behavior Knowledge Base
8.6 Operating System Health Monitor
8.7 Classification
8.8 Runtime Process of Anomaly Detection
8.9 Summary
9 Evaluation of Costs
9.1 Evaluation of the System Call Monitor
9.2 Evaluation of OS Health Monitor
9.3 Evaluation of Behavior Knowledge Base
9.4 Overhead of the Overall Approach
9.5 Summary
V Case Study
10 Evaluation Methodologies
10.1 Problems and Challenges
10.2 Requirements
10.3 Applicability of Virtual Reality and Virtual Environments
10.4 Summary
11 Evaluation Case Environment
11.1 Evaluation Environment
11.2 The BeBot
11.3 Interaction and Control
11.4 Evaluation Output
11.5 Evaluation Scenarios
11.6 Summary
12 Evaluation Results and Discussion
12.1 Evaluation Scenario 1
12.2 Evaluation Scenario 2
12.3 Evaluation Scenario 3
12.4 Evaluation Scenario 4
12.5 Discussion
VI Conclusion
13 Summary and Conclusion
Appendices
A ORCOS System Calls
A.1 Stream/File related system calls
A.2 Memory related system calls
A.3 Task related system calls
A.4 Thread related system calls
A.5 Signal related system calls
A.6 Socket related system calls
A.7 System calls for Task loading
A.8 Others
A.9 Specific system calls under QEMU
A.10 System calls for BeBot control
A.11 Additional System Calls for Bug Manipulator/Generator
B System Call Monitor API
C OS Health Monitor - Parameter
C.1 Scheduler Monitor
C.2 Processor Utilization Monitor
C.3 Memory Manager Monitor
C.4 Communication Monitor
C.5 File Manager Monitor
C.6 Device Driver Monitor
C.7 IR Sensor Monitor
D Device Driver Monitor Interface
E OS Health Monitor API
F Proposals for future research
F.1 Potentials to enhance the evaluation of the classification marker
F.2 Alternatives for Processing the Anomaly Detection
List of Figures
My Publications
Bibliography
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