Szczegóły
Tytuł artykułu
Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDSTytuł czasopisma
International Journal of Electronics and TelecommunicationsRocznik
2021Wolumin
vol. 67Numer
No 2Afiliacje
Shakeela, Shaikh : ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ; Shankar, N. Sai : ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ; Reddy, P Mohan : ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ; Tulasi, T. Kavya : ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ; Koneru, M. Mahesh : ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, IndiaAutorzy
Słowa kluczowe
ANOVA-F test ; Cross Validation ; Decision Trees ; Features ; NSL-KDD ; DatasetWydział PAN
Nauki TechniczneZakres
267-275Wydawca
Polish Academy of Sciences Committee of Electronics and TelecommunicationsBibliografia
[1] Ektefa, M. Mohammadreza, S. Sara and A. Fatimah, “Intrusion detection using data mining techniques,” 200 - 203. DOI: 10.1109/INFRKM.2010.5466919.[2] Y. Wang, W. Cai and P. Wei, “A Deep Learning Approach For Detecting Malicious Javascript Code,” Wiley Online Library . February 2016.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] Gulshan and Krishan, “A Multi-Objective Genetic Algorithm Based Approach For Effective Intrusion Detection Using Neural Networks,” Springer. 2015.
[8] 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).
[9] 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.
[10] 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).
[11] A. Preeti and S. Sudhir, “Analysis of KDD Dataset Attributes - Class wise for Intrusion Detection,” Procedia Computer Science, Volume 57, 2015, 842-851,
[12] 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.
[13] Campbell and Zachary, “Differentially Private ANOVA Testing,” 2018 1st International Conference on Data Intelligence and Security (ICDIS) (2018): 281-285.
[14] S. K. Murthy, “Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2, 345–389 (1998).
[15] S. Dhaliwal, A. Nahid and R. Abbas, “Effective Intrusion Detection System Using XGBoost. Information 2018, 9, 149.
[16] Pedregosa et al., “Scikit-learn: Machine Learning in Python,” JMLR 12, pp. 2825-2830, 2011.