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|dc.identifier.citation||2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021 , Vol. , , p. 936 - 941||en_US|
|dc.description.abstract||Application programming interfaces (APIs) are a vital part of every online business. APIs are responsible for transferring data across systems within a company or to the users through the web or mobile applications. Security is a concern for any public-facing application. The objective of this study is to analyze incoming requests to a target API and flag any malicious activity. This paper proposes a solution using sequence models to identify whether or not an API request has SQL, XML, JSON, and other types of malicious injections. We also propose a novel heuristic procedure that minimizes the number of false positives. False positives are the valid API requests that are misclassified as malicious by the model. © 2021 IEEE.||en_US|
|dc.title||Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests||en_US|
|Appears in Collections:||2. Conference Papers|
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