INFORMATION CHANGE THE WORLD

International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.11, No.10, Oct. 2019

An Efficiency Optimization for Network Intrusion Detection System

Full Text (PDF, 920KB), PP.1-11


Views:53   Downloads:3

Author(s)

Mahmoud M. Sakr, Medhat A. Tawfeeq, Ashraf B. El-Sisi

Index Terms

Intrusion Detection System;Network Anomaly Detection;Features Selection;Dimensionality Reduction;NSL-KDD;Swarm Intelligence

Abstract

With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

Cite This Paper

Mahmoud M. Sakr, Medhat A. Tawfeeq, Ashraf B. El-Sisi, "An Efficiency Optimization for Network Intrusion Detection System", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.10, pp.1-11, 2019.DOI: 10.5815/ijcnis.2019.10.01

Reference

[1]Statista. IoT - number of connected devices worldwide 2015-2025. https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/. Accessed: 01-05-2019. 

[2]N. Kaja, Adnan Shaout and Di Ma, “An intelligent intrusion detection system,” Applied Intelligence, pp. 1-13, 2019.

[3]C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel and M. Radaradan, “A Survey of Intrusion Detection Techniques in Cloud,” Journal of Network and Computer Applications, vol. 36, No.1, pp. 42–57, 2013.

[4]G. Fernandes, L. Fernando, J.F. Al-Muhadi and M. Lemes, “A comprehensive survey on network anomaly detection,” Telecommunication Systems, vol.70, no.3, pp. 447-489, 2019.

[5]Prachi, H. Malhotra and P. Sharma, “Intrusion Detection using Machine Learning and Feature Selection,” International Journal of Computer Network and Information security, vol.11, no.4, pp.43-52, 2019.

[6]M. Awad and R. Khanna, “Support Vector Machines for Classification Efficient Learning Machines,” Apress, Berkeley, California, pp. 39-66, 2015.

[7]Mahmoud M. Sakr, Medhat A. Tawfeeq and Ashraf B. El-Sisi, “Network Intrusion Detection System based PSO-SVM for Cloud Computing,” International Journal of Computer Network and Information Security, vol.11, no.3, pp.22-29, 2019.

[8]B. Hajimirzaei and N.J. Navimipour, “Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm,” ICT Express, vol.5, no.1, pp. 56-59, 2019.

[9]T. Khorram and N.A. Baykan, “Feature selection in network intrusion detection using metaheuristic algorithms,” International Journal of Advanced Research, Ideas and Innovations in Technology, vol.4, no.4, 2018.

[10]J. Jabez, S. Gowri, S. Vigneshwari, J.A. Mayan and S. Srinivasulu, “Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools,” Proceedings of ICTIS, vol. 2, pp. 675-682, 2018.

[11]B.M. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. Golkar and A. Ebrahimi, “A hybrid method consisting of GA and SVM for intrusion detection system,” Neural computing and applications, vol. 27, no. 6, pp. 1669-1676, 2019.

[12]H.M. Anwer, Mohamed Farouk and Ayman Abdel-Hamid, “A framework for efficient network anomaly intrusion detection with features selection,” In 9th IEEE International Conference on Information and Communication Systems, pp. 157-162, 2019.

[13]Nitu Dash, Sujata Chakravarty and Amiya Kumar Ratha, Intrusion Detection System Based on Principal Component Analysis and Machine Learning Techniques. International Journal of Engineering Development and Research (IJEDR) 2018. Vol.6, No.3.

[14]Jie Ling and Chengzhi Wu, “Feature Selection and Deep Learning-based Approach for Network Intrusion Detection,” In the 3rd International Conference on Mechatronics Engineering and Information Technology, 2019.    

[15]Sofiane Maza and Mohamed Touahria, “Feature Selection Algorithms in Intrusion Detection System: A Survey,” KSII Transactions on Internet and Information Systems, vol.12, no.10, pp.5079-5099, 2018.

[16]B. Senthilnayaki, K. Venkatalakshmi and K. Arputharaj, “Intrusion Detection System Using Feature Selection and Classification Technique,” In International Journal of Computer Science and Application, vol.3, no.4, pp.145, 2014.

[17]K. Keerthi Vasan and B. Surendiran, “Dimensionality reduction using Principal Component Analysis for network intrusion detection,” Perspectives in Science, vol.8, pp. 510-512, 2016.

[18]M.A. Hall and L.A. Smith, “Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper,” In the 12th international FLAIRS Conference, vol. 1999, pp. 235-239, 1999.

[19]K. Sanjay Desale and Roshani Ade, “Genetic algorithm based feature selection approach for effective intrusion detection system,” In IEEE International Conference on Computer Communication and Informatics,  pp 1-6, 2015.

[20]L. Dhanabal and S.P. Shantharadah, “A study on NSLKDD dataset for intrusion detection system based on classification algorithms,” International Journal of Advanced Research in Computer and Communication Engineering, vol.4, no.6, pp. 446–452, 2015. 

[21]E. Popoola and A. Adewumi, “Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision,” International Journal of Network Security, vol.19, no.5, pp. 660-669, 2017.

[22]T. Pham, E. Foo, S. Suriadi and H. Jeffrey, “Improving performance of intrusion detection system using ensemble methods and feature selection,” Australasian Computer Science Week Multi-Conference, ACM, pp.1-6, 2018.

[23]N.K. Kanakarajan and K. Muniasamy, “Improving the accuracy of intrusion detection using gar-forest with feature selection,” Advances in Intelligent Systems and Computing, vol.404, pp. 539-547, 2016.

[24]H.H. Pajouh, G.H. Dastghaibyfard, and S. Hashemi, Two-tier network anomaly detection model: a machine learning approach. Journal of Intelligent Information Systems, 2017. Vol.48, No.1, pp. 61–74. 

[25]D. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim and K. J. Kim, A survey of deep learning-based network anomaly detection. Cluster Computing, 2017. Vol. 4, No. 3, pp. 1-13.

[26]T. Khorram and N.A. Baykan, Feature selection in network intrusion detection using metaheuristic algorithms. International Journal of Advanced Research, Ideas and Innovations in Technology, 2016. Vol.4, No.4, pp.704-710. 

[27]P. Kar, S. Banerjee, K.C. Mondal, G. Mahapatra and S. Chattopadhyay, A Hybrid Intrusion Detection System for Hierarchical Filtration of Anomalies. Information and Communication Technology for Intelligent Systems, Springer, Singapore, 2019. pp.417-426.‏ 

[28]Kazi Abu Taher, B.MY. Jisan and Md. M Rahman, Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection. In IEEE International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp.643-646, 2019. 

[29]Partha Ghosh, A.K. Mandal and Rupesh Kumar, An Efficient Cloud Network Intrusion Detection System.Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing, Springer, 2015. vol. 339, pp. 91-99. 

[30]M.G. Raman, N. Somu, K. Kirthivasan, R. Liscano, V.S. Sriram, An efficient intrusion detection system based on hypergraph - genetic algorithm for parameter optimization and feature selection in support vector machine, Knowlege. Based System. Vol.134 (2017) pp. 1–12. 

[31]X.Zhang, J.Tian, P.Zhu and J.Zhang, An Effective Semi-Supervised Model for Intrusion Detection Using Feature Selection Based LapSVM, In IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 283-286, 2017.

[32]I.S. Thaseen and Ch.A. Kumar, Intrusion Detection Model Using Chi Square Feature Selection and Modified Naïve Bayes Classifier, In Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC--16’), pp. 81-91, 2016.

[33]Adriana-Cristina Enache1, V. Sgarciu and A. Petrescu-Ni¸ta, Intelligent Feature Selection Method rooted in Binary Bat Algorithm for Intrusion Detection, In IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, pp. 517-521, 2015. 

[34]Ingrid Russell and Zdravko Markov, “An introduction to the Weka data mining system,” In Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education, vol.38, no.3, pp. 367-368, 2006.