Abstract
Techniques are described herein for detecting anomalies in Application Programming Interface (API) request/response/payloads and determining probable root causes by analyzing the log, request payload, response payload, and metric data of the hosts serving the API in the Internet of Things (IoT) infrastructure. The API response time is captured in the log messages and the anomalies are detected using machine learning and statistical techniques. Given the anomalies in API response time and the probable root cause in real time, the IoT administrator may identify the root cause without manual effort and remediate the anomaly by taking appropriate action. Thus, the techniques described herein enable identification of treatments for API abuse from users or system failures because of a chain reaction on a host.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Malikireddy, Sai Kiran Reddy; Jain, Lalit; Algubelli, Bipin; Veluru, Chandra; Parekh, Vivek; Kumar, Dileep; and Suba, Vimal, "AUTOMATED ROOT CAUSE ANALYSIS FOR MITIGATING RISK TO APPLICATION PROGRAMMING INTERFACES IN INTERNET OF THINGS INFRASTRUCTURE", Technical Disclosure Commons, (April 12, 2019)
https://www.tdcommons.org/dpubs_series/2140