Abstract
Techniques are described for identifying key parameters for classifying interactive clients and daemon clients. Machine learning algorithms based on Principal Component Analysis (PCA) and enhanced Decision Tree (DT) are utilized together with HyperText Transfer Protocol (HTTP) header analysis to accurately classify clients. Rate-limiting effects are monitored as a feedback to perform re-enforcement learning to improve accuracy of the algorithm. The Application Programming Interface (API) gateway is one of the most important components to any cloud service and it needs to apply different rate-limiting policies on API requests based on different requirements of different types of clients. Traditional client identification with source Internet Protocol (IP) addresses are no longer suitable when moving a networking infrastructure to a cloud platform.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ma, Xueqiang; Sankaralingam, Kumar; Chattopadhyay, Shubhojit; and Ghosh, Kalyan, "AUTOMATED CLIENT CLASSIFICATION AND POLICY ENFORCEMENT THROUGH CLOUD APPLICATION PROGRAMMING INTERFACE GATEWAY", Technical Disclosure Commons, (December 04, 2018)
https://www.tdcommons.org/dpubs_series/1755