Elastic Information Flow Tracking for Program Behavior Analysis

• Main Authors: Chiu, Yi-Pin, 邱奕斌
• Other Authors: 吳育松
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/m37t37

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 104 === Open source project has been widely developed and released thanks to the rapid growth of the Internet. Many useful open source projects can be found on the Internet. However, using open source project might be insecure even though it provides all the source code. The reason is that for users who just want to use the program as a tool, they might not actually check the containing code. The execution behavior of the program might not be the same as you think. Hence, in this research, we propose an elastic information flow tracking framework based on source code transformation. Our system can automatically transform the target source code to apply elastic information flow tracking capability. Users can use the transformed program to monitor the information flows within the project. We also use the machine learning technique to analyze the information extracted from program runtime and try to infer the underlying program behavior. Our framework can help user to have the knowledge of the program and make sure the program is executing as expected. We also provide some flexible mechanisms in our research. Users can decide the data types they want to track. And we have a dynamic switch mechanism let users to turn on or turn off the tracking process during program runtime to reduce the performance overhead. Evaluation results show that our system can help users to monitor the information flows within the program and also has the effectiveness to analyze the program behavior.