INFLUENCE OF PRE-PROCESSING ON ANOMALY-BASED INTRUSION DETECTION

Introduction/purpose: The anomaly-based intrusion detection system detects intrusions based on a reference model which identifies the normal behavior of a computer network and flags an anomaly. Machine-learning models classify intrusions or misuse as either normal or anomaly. In complex computer networks, the number of training records is large, which makes the evaluation of the classifiers computationally expensive. Methods: A feature selection algorithm that reduces the dataset size is presented in this paper. Results: The experiments are conducted on the Kyoto 2006+ dataset and four classifier models: feedforward neural network, k-nearest neighbor, weighted k-nearest neighbor, and medium decision tree. The results show high accuracy of the models, as well as low false positive and false negative rates. Conclusion: The three-step pre-processing algorithm for feature selection and instance normalization resulted in improving performances of four binary classifiers and in decreasing processing time.