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Vulnerable Code Clone Detection for Operating System Through Correlation-Induced Learning
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DOI：10.1109/TII.2019.2929739.png)
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Abstract
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Vulnerable code clones in the operating system (OS) threaten the safety of smart industrial environment, and most vulnerable OS code clone detection approaches neglect correlations between functions that limits the detection effectiveness. In this article, we propose a two-phase framework to find vulnerable OS code clones by learning on correlations between functions. On the training phase, functions as the training set are extracted from the latest code repository and function features are derived by their AST structure. Then, external and internal correlations are explored by graph modeling of functions. Finally, the graph convolutional network for code clone detection (GCN-CC) is trained using function features and correlations. On the detection phase, functions in the to-be-detected OS code repository are extracted and the vulnerable OS code clones are detected by the trained GCN-CC. We conduct experiments on five real OS code repositories, and experimental results show that our framework outperforms the state-of-the-art approaches.
Keywords:
Cloning
Correlation
Training
Feature extraction
Detectors
Informatics
Operating systems
Cyber-physical security
correlation modeling
graph convolutional network
vulnerable code clone detection
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