Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

Shaikh Shakeela, Nandigama Sai Shankar, P Mohan Reddy, T Kavya Tulasi, M Mahesh Sai

Abstract


Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method.


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References


Ektefa, M. Mohammadreza, S. Sara and A. Fatimah, “Intrusion detection using data mining techniques,” 200 - 203. 10.1109/INFRKM.2010.5466919.

Y. Wang, W. Cai and P. Wei, “A Deep Learning Approach For Detecting Malicious Javascript Code,” Wiley Online Library . February 2016.

C. Yin , Y. Zhu, J. Fei and H. Xinzheng, “A Deep Learning Approach For Intrusion Detection Using Recurrent Neural Networks,” IEEE Access. November 7, 2017.

Q. Niyaz, W. Sun, Y Javaid and A. Mansoor, “A Deep Learning Approach For Network Intrusion Detection system,” In Eai Endorsed Transactions on Security and Safety, Vol. 16, Issue 9, 2016.

M. Preeti, V. Vijay, T. Uday and S. P. Emmanuel, “A Detailed Investigation And Analysis Of Using Machine Learning Techniques For Intrusion Detection,” IEEE Communications Surveys & Tutorials, Volume: 21, Issue:1, First quarter 2019.

Y. Li, M. Rong And R. Jiao, “A Hybrid Malicious Code Detection Method Based On Deep Learning,” International Journal of Software Engineering and Its Applications 9(5):205-216, May 2015.

Gulshan and Krishan, “A Multi-Objective Genetic Algorithm Based Approach For Effective Intrusion Detection Using Neural Networks,” Springer. 2015.

K. Levent and D. C. Alan, “Network Intrusion Detection Using A Hidden Naïve Bayes Binary Classifier,” 2015 17th Uksim-Amss International Conference on Modelling and Simulation (Uksim).

A. Nadjaran, K. Mohsen, “A New Approach To Intrusion Detection Based On An Evolutionary Soft Computing Model Using Neuro-Fuzzy Classifiers,” July 2007, Computer Communications 30(10):2201-2212.

D. Amin and R Mahmood, “Feature Selection Based On Genetic Algorithm And Support Vector Machine For Intrusion Detection System,” The Second International Conference On Informatics Engineering & Information Science (Icieis2013).

A. Preeti and S. Sudhir, “Analysis of KDD Dataset Attributes - Class wise for Intrusion Detection,” Procedia Computer Science, Volume 57, 2015, 842-851.

D. M. Doan, D. H. Jeong and S. Ji, “Designing a Feature Selection Technique for Analyzing Mixed Data,” 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020, pp. 0046-0052, doi: 10.1109/CCWC47524.2020.9031193.

Campbell and Zachary, “Differentially Private ANOVA Testing,” 2018 1st International Conference on Data Intelligence and Security (ICDIS) (2018): 281-285.

S. K. Murthy, “Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2, 345–389 (1998).

S. Dhaliwal, A. Nahid and R. Abbas, “Effective Intrusion Detection System Using XGBoost. Information 2018, 9, 149.

Pedregosa et al., “Scikit-learn: Machine Learning in Python,” JMLR 12, pp. 2825-2830, 2011.


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