Select the Best Machine Learning Algorithms for Prediction and Classification of Intrusions using KDD99 Intrusion Detection Dataset

Sara Tamy sup; Hicham Belhadaoui; Mahmoud Almostafa Rabbah sup; Nabila Rabbah  and Mounir Rifi  nbsp

doi:10.17485/ijst/2019/v12i37/147551

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Select the Best Machine Learning Algorithms for Prediction and Classification of Intrusions using KDD99 Intrusion Detection Dataset

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2019/v12i37/147551

Year: 2019, Volume: 12, Issue: 37, Pages: 1-6

Original Article

Select the Best Machine Learning Algorithms for Prediction and Classification of Intrusions using KDD99 Intrusion Detection Dataset

Sara Tamy¹*, Hicham Belhadaoui¹, Mahmoud Almostafa Rabbah¹, Nabila Rabbah² and Mounir Rifi¹

¹RITM Laboratory, ESTC, Hassan II University, BP. 8012, Casablanca, Morocco; [email protected], [email protected], [email protected], [email protected] ²Laboratory of Structural Engineering, Intelligent Systems and Electrical Energy, ENSAM, Hassan II University, BP. 20000, Casablanca, Morocco; [email protected]

*Author for correspondence
Sara Tamy
RITM Laboratory, ESTC, Hassan II University, BP. 8012, Casablanca, Morocco; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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)