Browse > Article
http://dx.doi.org/10.22937/IJCSNS.2022.22.5.15

Intrusion Detection using Attribute Subset Selector Bagging (ASUB) to Handle Imbalance and Noise  

Priya, A.Sagaya (Department of Computer Science, St. Joseph's College (Autonomous), Affiliated to Bharathidasan University)
Kumar, S.Britto Ramesh (Department of Computer Science, St. Joseph's College (Autonomous), Affiliated to Bharathidasan University)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.5, 2022 , pp. 97-102 More about this Journal
Abstract
Network intrusion detection is becoming an increasing necessity for both organizations and individuals alike. Detecting intrusions is one of the major components that aims to prevent information compromise. Automated systems have been put to use due to the voluminous nature of the domain. The major challenge for automated models is the noise and data imbalance components contained in the network transactions. This work proposes an ensemble model, Attribute Subset Selector Bagging (ASUB) that can be used to effectively handle noise and data imbalance. The proposed model performs attribute subset based bag creation, leading to reduction of the influence of the noise factor. The constructed bagging model is heterogeneous in nature, hence leading to effective imbalance handling. Experiments were conducted on the standard intrusion detection datasets KDD CUP 99, Koyoto 2006 and NSL KDD. Results show effective performances, showing the high performance of the model.
Keywords
Intrusion detection; Ensemble modelling; Bagging; Data Imbalance; Noise;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 G.C. Tjhai, S.M. Furnell, M. Papadaki, N.L. Clarke, A preliminary two-stage alarm correlation and filtering system using som neural network and k -means algo- rithm, Comput. Security 29 (6) (2010) 712-723. http://dx.doi.org/10.1016/j.cose.2010.02.001.   DOI
2 Somasundaram, A. and Reddy, U.S., 2017, June. Modelling a stable classifier for handling large scale data with noise and imbalance. In 2017 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). IEEE.
3 Akila, S. and Reddy, U.S., 2016. Data imbalance: effects and solutions for classification of large and highly imbalanced data. Proceedings of ICRECT, 16, pp.28-34.
4 Chellammal, P., and Sheba Kezia PD Malarchelvi. "Real-time anomaly detection using parallelized intrusion detection architecture for streaming data." concurrency and computation-practice & experience 32, no. 4 (2020).
5 Intrusion Detection in Computer Networks using Lazy Learning Algorithm
6 Samuel Marchal, Xiuyan Jiangz, Radu State, Thomas Engel (2014) "A Big Data Architecture for Large Scale Security Monitoring" , Springer.
7 G. Liu, F. Hu, W. Chen, A neural network ensemble based method for detecting computer virus, in: 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Vol. 1, Aug 2010, pp. 391-393.
8 J.Wu, D. Peng, Z. Li, L. Zhao, H. Ling, Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm, PLOS ONE 10 (3) (2015) 1-13.
9 Kumar, V. D., & Radhakrishnan, S. (2014, April). Intrusion detection in MANET using self organizing map (SOM). In Recent Trends in Information Technology (ICRTIT), 2014 International Conference on (pp. 1-8). IEEE.
10 X.-S. Yang,"Firefly algorithm, Levy flights and global optimization", in: Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer London, pp. 209-218 (2010)
11 S. Revathi and A. Malathi, "Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques," International Journal of Computer Applications , Volume 75- No.6, August 2013
12 Kalita, D.J., Singh, V.P. and Kumar, V., 2020. SVM Hyper-Parameters Optimization using Multi-PSO for Intrusion Detection. In Social Networking and Computational Intelligence (pp. 227-241). Springer, Singapore.
13 Point Biserial Coefficient (Keith Calkins, 2005)
14 Intelligent intrusion detection systems using artificial neural networks
15 Sung-Hwan Ahn, Nam-Uk Kim,Tai-Myoung Chung (2014) "Big Data Analysis System Concept for Detecting Unknown Attacks", IEEE.
16 C.F. Tsai, Y.F. Hsu, C.Y. Lin, W.Y. Lin, Intrusion detection by machine learning: a review, Expert Syst. Appl. Int. J. 36 (10) (2009) 11994-12000. http://dx.doi.org/10.1016/j.eswa.2009.05.029.   DOI
17 H.J. Liao, C.H.R. Lin, Y.C. Lin, K.Y. Tung, Intrusion detection system: a compre- hensive review, J. Netw. Comput. Appl. 36 (1) (2013) 16-24. http://dx.doi.org/10.1016/j.jnca.2012.09.004.   DOI
18 An effective intrusion detection framework based on SVM with feature augmentation
19 B. Luo, J. Xia, A novel intrusion detection system based on feature generation with visualization strategy, Expert Syst. Appl. 41 (9) (2014) 4139-4147. http://dx.doi.org/10.1016/j.eswa.2013.12.048.   DOI
20 Network Intrusion Detection in Big Dataset Using Spark
21 Dimensionality Reduction with IG-PCA and Ensemble Classifier for Network Intrusion Detection
22 Firefly algorithm based Feature Selection for Network Intrusion Detection
23 Jiang, H., He, Z., Ye, G. and Zhang, H., 2020. Network Intrusion Detection Based on PSO-Xgboost Model. IEEE Access.
24 Unsupervised intrusion detection through skip-gram models of network behavior