Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests

Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/14785

Full metadata record

DC Field	Value	Language
dc.contributor.author	Reddy S.A.
dc.contributor.author	Rudra B.
dc.date.accessioned	2021-05-05T10:15:46Z	-
dc.date.available	2021-05-05T10:15:46Z	-
dc.date.issued	2021
dc.identifier.citation	2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021 , Vol. , , p. 936 - 941	en_US
dc.identifier.uri	https://doi.org/10.1109/CCWC51732.2021.9376034
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/14785	-
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
dc.type	Conference Paper	en_US
Appears in Collections:	2. Conference Papers

Files in This Item:

There are no files associated with this item.