Supervised Intrusion Detection with Out-of-Distribution Detection for Microservices
Yong-Syuan Chen, Hsiang-Yin Lien, Jo-Yu Li, Chia‐Yu Lin · 2024
Microservice architecture enhances system flexibility and reliability but raises security concerns due to potential malicious attacks. We propose a supervised Out-of-Distribution (OOD) detector leveraging AI and ML to analyze container command sequences. Our technique identifies known and unknown attack patterns, employing out-of-distribution detection. Using a deep neural network, we learn features and minimize classification errors. Comparative evaluations demonstrate its efficacy, aiming to enhance container security and deepen insights into microservice attack behaviors.