FUR-API: Dataset and Baselines Toward Realistic API Anomaly Detection

The Application Program Interface (API) security is crucial for data security as it ensures the safety and authority of data exchange between different applications. However, the absence of high-quality datasets significantly impedes the development of API anomaly detection. This paper presents a benchmark dataset and baselines for realistic API anomaly detection involving few-shot and unknown-risk scenarios. The dataset is synthesized using an Iterative data Generation approach with Dual-channel Filtering (IGDF). By leveraging large language models and dual-channel filtering models, we can iteratively generate and filter data, yielding a high-quality dataset. Moreover, we have developed baselines and conducted extensive experiments on the proposed dataset. The results indicate that few-shot and unknown-risk API anomaly detection remains a challenging task and still requires further research. All details and resources are released at https://github.com/yijunL/FUR-API.