Recurrent Neural Network and Convolutional Neural Network for Detection of Denial of Service Attack in Microservices

Containerized Cloud computing system is a lightweight virtualization platform to build and deploy applications easily. Especially it can help to mitigate many of the challenges associated with microservices architecture. However, with the help of high scalability of lightweight container servers, microservices build complex internal hierarchy providing unknown attack surfaces where attackers can exploit. In this work, we show a microservice specific Denial of Service attack that exploits complexity of internal traffics between microservices. To detect malicious activities apparently looking normal and legal, we applied both Recurrent Neural Network model trained from the dataset of sequential TCP sessions, and Convolutional Neural Network model trained from the dataset of connection trees among microservices. As a result, we obtained high detection rates, which are 98.4% for the Recurrent Neural Network and 97.0% for the Convolutional Neural Network respectively and low false detection rates.