Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems
This work addresses the need for efficient intrusion detection in computer and network systems, but it is incremental as it combines existing methods with feature selection.
The authors tackled the problem of optimizing intrusion detection system performance by designing a multi-layer model that uses machine learning techniques with gain ratio for feature selection, achieving higher classification accuracy and lower false alarm rates, particularly with C5 decision tree.
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 work, A multi-layer intrusion detection model is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy .we effectively apply Machine learning techniques (C5 decision tree, Multilayer Perceptron neural network and Naïve Bayes) using gain ratio for selecting the best features for each layer as to use smaller storage space and get higher Intrusion detection performance. Our experimental results showed that the proposed multi-layer model using C5 decision tree achieves higher classification rate accuracy, using feature selection by Gain Ratio, and less false alarm rate than MLP and naïve Bayes. Using Gain Ratio enhances the accuracy of U2R and R2L for the three machine learning techniques (C5, MLP and Naïve Bayes) significantly. MLP has high classification rate when using the whole 41 features in Dos and Probe layers.