| Title | Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | A, Sujan Reddy, Rudra, Bhawana |
| Conference Name | 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) |
| Keywords | API, APIs, Application program interface, application program interfaces, Application Programming Interface (API), Bidirectional Recurrent Neural Networks, composability, compositionality, Computational modeling, Conferences, Gated Recurrent Units, long short term memory, mobile applications, pubcrawl, Recurrent neural networks, resilience, Resiliency, security, Training, Vanilla Recurrent Neural Networks, XML |
| 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. |
| DOI | 10.1109/CCWC51732.2021.9376034 |
| Citation Key | a_evaluation_2021 |
Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests