Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests

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dc.contributor.authorReddy, S.A.
dc.contributor.authorRudra, B.
dc.date.accessioned2026-02-06T06:35:58Z
dc.date.issued2021
dc.description.abstractApplication 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.
dc.identifier.citation2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021, 2021, Vol., , p. 936-941
dc.identifier.urihttps://doi.org/10.1109/CCWC51732.2021.9376034
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30175
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAP
dc.subjectI Bidirectional Recurrent Neural Networks
dc.subjectGated Recurrent Units
dc.subjectLong short term memory
dc.subjectSecurity
dc.subjectVanilla Recurrent Neural Networks
dc.titleEvaluation of Recurrent Neural Networks for Detecting Injections in API Requests

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