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Boosting the Performance of Alias-Aware IFDS Analysis with CFL-Based Environment Transformers

Year: 2024
Type: article
Abstract: The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its conventional use of access paths for field sensitivity leads to the generation of a large number of data-flow facts. This causes scalability challenges in larger programs, limiting its practical application in extensive codebases. In response, we propose a new field-sensitive technique that reinterprets the generation of access paths as a Context-Free Language (CFL) for field-sensitivity and formulates it as an IDE problem. This approach significantly reduces the number of data-flow facts generated and handled during the analysis, which is a major factor in performance degradation. To demonstrate the effectiveness of this approach, we developed a taint analysis tool, IDEDroid, in the IFDS/IDE framework. IDEDroid outperforms FlowDroid, an established IFDS-based taint analysis tool, in the analysis of 24 major Android apps while improving its precision (guaranteed theoretically). The speed improvement ranges from 2.1 × to 2,368.4 × , averaging at 222.0 × , with precision gains reaching up to 20.0 % (in terms of false positives reduced). This performance indicates that IDEDroid is substantially more effective in detecting information-flow leaks, making it a potentially superior tool for mobile app vetting in the market.
DOI: 10.1145/3689804
Language: English
Open Access status: diamond
OpenAlex ID: W4403223035
FWCI: 0.887
Cites: 70
Cited by: 6
Related to: 10