Adaptive Layered Approach Using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems
Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this paper, A multi-layer intrusion detection model is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy. The authors effectively apply Machine learning techniques (C5 decision tree, Multilayer Perceptron neural network and Na?ve Bayes) using gain ratio for selecting the best features for each layer as to use smaller storage space and get higher Intrusion detection performance.