Intrusion Detection and Attack Classification Leveraging Machine Learning Technique

Abstract

Due to the advancement in information exchange over the Internet and mobile technologies, malicious network attacks have significantly increased. Machine learning algorithms can play a vital role in network security and attacks classification. This paper compares two different types of classifiers (Naive Bayes and Decision Tree) for the intrusion detection system on the publicly available dataset. Simulations are carried out using the WEKA machine learning tool and experimentation is performed on full data and selected features using subset evaluator algorithm. The classifier performance is evaluated in terms of accuracy, specificity, recall, precision, f1-score, error rates and response time. Naive Bayes classifier performance was better in terms of computational time, however, the accuracy, error rate, f1-score, and recall values of Decision Tree were better than Naive Bayes.