ANALYZING THE MACHINE LEARNING ALGORITHMS- NAÏVE BAYES, RANDOM TREE, AND SUPPORT VECTOR MACHINES SVM USING THE KDD99 DATA SET TO PREDICT AND CLASSIFY THE INTRUSION DETECTION SYSTEM USING WEKA

Saumya Gupta

Vol. 2, Jan-Dec 2016

Page Number: 452 - 459

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 special access or attacks to secure internal networks. In this study, we will outline the evolution of extensive 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

References

• Panda M, Patra MR. Network intrusion detection using Naive bayes, International Journal of Computer Science and Network Security. 2007; 7(12):258−63
• Gudadhe M, Prasad P, Wankhade K. A new data mining based network intrusion detection model. In: 2010 International Conference on Computer and Communication Technology; 2010. p. 731−35. https://doi. org/10.1109/ICCCT.2010.5640375.
• Sinclair C, Pierce L, Matzner S. An application of machine learning to network intrusion detection. In: Proceedings 15th Annual Computer Security Applications Conference; 1999. p. 371−77.
• Sara T, Rabbah N, Rabbah MA. Study of Strategies for Real-Time Supervision of Industrial Network Security; 2018. p. 1−5.
• Yu Z, Tsai JJP. A framework of machine learning based intrusion detection for wireless sensor networks. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing; 2008. p. 272−79. https://doi. org/10.1109/SUTC.2008.39. PMCid: PMC2604134.
• Pietraszek T, Tanner A. Data mining and machine learning- towards reducing false positives in intrusion detection, Information Security Technical Report. 2005; 10(3):169−83. https://doi.org/10.1016/j.istr.2005.07.001
• Panda M, Abraham A, Patra MR. A hybrid intelligent approach for network intrusion detection, Procedia Engineering. 2012; 30:1−9. https://doi.org/10.1016/j.proeng. 2012.01.827.
• Khourdifi Y, Bahaj M. Selecting Best Machine Learning Techniques for Breast Cancer Prediction and Diagnosis. In: International Conference Europe Middle East and North Africa Information Systems and Technologies to Support Learning. Springer, Cham.; 2018. p. 565−71. https://doi. org/10.1007/978-3-030-03577-8_61.
• Witten IH, Frank E, Trigg LE. WEKA: Practical machine learning tools and techniques with Java implementations; 1999. p. 1−5
• Özgür A, Erdem H. A review of KDD99 datasetusage in intrusion detection and machine learning between 2010 and 2015, Peer. J. Preprints. 2016; 4:1−22. https://doi.org/10.7287/peerj.preprints.1954.
• Rish I. An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence; 2001. p. 41−46
• Kalmegh S. Analysis of WEKA data mining algorithm rep tree, simple cart and random tree for classification of Indian news, International Journal of Innovative Science, Engineering and Technology. 2015; 2(2):438−46.
• Osuna E, Freund R, Girosit F. Training support vector machines: An application to face detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 1997. p. 130−36.

Back Download