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This paper presents an evaluation of the code representation model Code2vec when trained on the task of detecting security vulnerabilities in C source code. We leverage the open-source library astminer to extract path-contexts from the abstract syntax trees of a corpus of labeled C functions. Code2vec is trained on the resulting path-contexts with the task of classifying a function as vulnerable or non-vulnerable. Using the CodeXGLUE benchmark, we show that the accuracy of Code2vec for this task is comparable to simple transformer-based methods such as pre-trained RoBERTa, and outperforms more naive NLP-based methods. We achieved an accuracy of 61.43% while maintaining low computational requirements relative to larger models.
Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mech
Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure, requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hen
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inef
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