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2021-01-20
Li, H., Xie, R., Kong, X., Wang, L., Li, B..  2020.  An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts? 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :479—488.

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

2018-04-11
Bronte, Robert, Shahriar, Hossain, Haddad, Hisham M..  2017.  Mitigating Distributed Denial of Service Attacks at the Application Layer. Proceedings of the Symposium on Applied Computing. :693–696.

Distributed Denial of Service (DDoS) attacks on web applications have been a persistent threat. Existing approaches for mitigating application layer DDoS attacks have limitations such low detection rate and inability to detect attacks targeting resource files. In this work, we propose Application layer DDoS (App-DDoS) attack detection framework by leveraging the concepts of Term Frequency (TF)-Inverse Document Frequency (IDF) and Latent Semantic Indexing (LSI). The approach involves analyzing web server logs to identify popular pages using TF-IDF; building normal resource access profile; generating query of accessed resources; and applying LSI technique to determine the similarity between a given session and known good sessions. A high-level of dissimilarity triggers a DDoS attack warning. We apply the proposed approach to traffics generated from three PHP applications. The initial results suggest that the proposed approach can identify ongoing DDoS attacks against web applications.