Select the Best Machine Learning Algorithms for Prediction and Classification of Intrusions using KDD99 Intrusion Detection Dataset
Sara Tamy, Hicham Belhadaoui, Mahmoud Almostafa Rabbah, Nabila Rabbah, Mounir Rifi · Indian Journal of Science and Technology · 2019
Objectives/Methods: The growing prevalence of network attacks is an issue that can affect the availability, confidentiality and integrity of critical information for companies. Thus, Intrusion detection systems are increasingly being used to identify unusual access or attacks to secure internal networks. In this study, we will outline the evolution of large data in the intrusion detection system, and apply three supervised learning methods namely: Naïve Bayes, Random tree, and Support Vector Machines SVM, using the kdd99 data set. The purpose of this research is to detect and predict attacks in order to take preventive action against intrusion risks. Findings: Investigational results have demonstrated that the random tree gives the highest accuracy at 100%. The results will be useful in choosing the best classification machine learning algorithm for intrusion prediction. Application/Improvements: for simulation and testing the performance of algorithms, we have used WEKA (Waikato environment for knowledge analysis), which includes tools for data preparation, classification, regression, clustering, association rule extraction and visualization. Keywords: Intrusion Detection System (IDS), Machine Learning, KDD99, Naïve Bayes, Random Tree, Support Vector Machines (SVM)