Background/Introduction RA flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically-inspired machine learning based approach is proposed in this study to detect RA flooding attacks. Methods The proposed technique exploits information gain ratio (IGR) and Principal Component Analysis (PCA) for feature selection and a Support Vector Machine (SVM) based predictor model, which can also detect input traffic anomaly.