<div>
	Dimensionality reduction of the attributes using fuzzy optimized independent component analysis for a Big Data Intrusion Detection System</div>

Rajan Aswanandini, Chandran Deepa

doi:10.17586/2226-1494-2022-22-1-93-100

2022 , VOLUME 22, NUMBER 1 ( january-february )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

Publications

Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.

Partners

doi: 10.17586/2226-1494-2022-22-1-93-100

Dimensionality reduction of the attributes using fuzzy optimized independent component analysis for a Big Data Intrusion Detection System

R. Aswanandini, C. Deepa

Read the full article

Article in English

For citation:

Aswanandini R., Deepa Ch. Dimensionality reduction of the attributes using fuzzy optimized independent component analysis for a Big Data Intrusion Detection System. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 1, pp. 93–100. doi: 10.17586/2226-1494-2022-22-1-93-100

Abstract

Big data cybersecurity has garnered more attraction in recent years with the development of advanced machine learning and deep learning classifiers. These new classifier algorithms have significantly improved Intrusion Detection Systems (IDS). In these classifiers, the performance is positively influenced by high relevant features while less relevant features negatively influence the performance. However, considering all the attributes, especially the high dimensional attributes, increases computational complications. Hence it is essential to diminish the dimensionality of the attributes to improve the classifier performance. To achieve this objective, an efficient dimensionality reduction approach is presented through the development of the Fuzzy Optimized Independent Component Analysis (FOICA) technique. The standard Independent Component Analysis (ICA) is coupled with the fuzzy entropy to transform the high dimension attributes into low dimension attributes and helps in selecting high informative low-dimensional attributes. These selected features are fed to efficient hybrid classifiers namely Hyper-heuristic Support Vector Machines (HH-SVM), Hyper-Heuristic Improved Particle Swarm Optimization based Support Vector Machines (HHIPSO-SVM) and Hyper-Heuristic Firefly Algorithm based Convolutional Neural Networks (HHFA-CNN) to classify the cybersecurity data to identify the intrusions. Experiments are conducted over two cybersecurity datasets and real-time laboratory data whose outcomes specify the supremacy of the suggested IDS model based on FOICA dimensionality reduction.

Keywords: big intrusion data, cybersecurity, intrusion detection system, independent component analysis, dimensionality reduction, hyper-heuristic firefly algorithm, convolutional neural networks, NSL-KDD

References

Liao H.J., Lin C.H.R., Lin Y.C., Tung K.Y. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 2013, vol. 36, no. 1, pp. 16–24.https://doi.org/10.1016/j.jnca.2012.09.004
Ashoor A.S., Gore S. Importance of intrusion detection system (IDS). International Journal of Scientific and Engineering Research, 2011, vol. 2, no. 1, pp. 1–4.
Wang Q., Lu P. Research on application of artificial intelligence in computer network technology. International Journal of Pattern Recognition and Artificial Intelligence, 2019, vol. 33, no. 5, pp. 1959015.https://doi.org/10.1142/S0218001419590158
Chen T.M., Walsh P.J.Guarding against network intrusions. Computer and Information Security Handbook, 2013, pp. 81–95. https://doi.org/10.1016/B978-0-12-394397-2.00005-2
Khraisat A., Gondal I., Vamplew P., Kamruzzaman J. Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2019, vol. 2, no. 1, pp. 20.https://doi.org/10.1186/s42400-019-0038-7
Liu H., Lang B. Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 2019, vol. 9, no. 20, pp. 4396.https://doi.org/10.3390/app9204396
Sultana N., Chilamkurti N., Peng W., Alhadad R. Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 2019, vol. 12, no. 2, pp. 493–501.https://doi.org/10.1007/s12083-017-0630-0
Sandhu U.A., Haider S., Naseer S., Ateeb O.U. A survey of intrusion detection & prevention techniques. Proc. of the 2011 International Conference on Information Communication and Management (IPCSIT).Vol. 16, 2011, pp. 66–71.
Aldweesh A., Derhab A., Emam A.Z. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 2020, vol. 189, pp. 105124.https://doi.org/10.1016/j.knosys.2019.105124
Reddy G.T., Reddy M.P.K., Lakshmanna K., Kaluri R., Rajput D.S., Srivastava G., Baker T. Analysis of dimensionality reduction techniques on big data. IEEE Access, 2020, vol. 8, pp. 54776–54788.https://doi.org/10.1109/ACCESS.2020.2980942
Varma P.R.K., Kumari V.V., Kumar S.S. A survey of feature selection techniques in intrusion detection system: A soft computing perspective. Advances in Intelligent Systems and Computing,2018, vol. 710, pp. 785–793. https://doi.org/10.1007/978-981-10-7871-2_75
Almusallam N.Y., Tari Z., Bertok P., Zomaya A.Y. Dimensionality reduction for intrusion detection systems in multi-data streams—A review and proposal of unsupervised feature selection scheme. Emergence Complexity and Computation, 2017, vol. 24, pp. 467–487.https://doi.org/10.1007/978-3-319-46376-6_22
Sabar N.R., Yi X., Song A. A bi-objective hyper-heuristic support vector machines for big data cyber-security. IEEE Access, 2018, vol. 6, pp. 10421–10431.https://doi.org/10.1109/ACCESS.2018.2801792
Aswanandini R., Muthumani N. Multi-objective hyper-heuristic improved particle swarm optimization based configuration of support vector machines for big data cyber security. International Journal of Innovative Technology and Exploring Engineering, 2019, vol. 8, no. 12, pp. 3892–3897.https://doi.org/10.35940/ijitee.L3401.1081219
Vasan K.K., Surendiran B. Dimensionality reduction using principal component analysis for network intrusion detection. Perspectives in Science, 2016, vol. 8, pp. 510–512.https://doi.org/10.1016/j.pisc.2016.05.010
Salo F., Nassif A.B., Essex A. Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Computer Networks, 2019, vol. 148, pp. 164–175.https://doi.org/10.1016/j.comnet.2018.11.010
Moustakidis S., Karlsson P. A Novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection. Cybersecurity, 2020, vol. 3, no. 1, pp. 16.https://doi.org/10.1186/s42400-020-00056-4
Zhou Y., Cheng G., Jiang S., Dai M. Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks, 2020, vol. 174, pp. 107247.https://doi.org/10.1016/j.comnet.2020.107247
Swarna Priya R.M., Maddikunta P.K.R., Parimala M., Koppu S., Gadekallu T.R., Chowdhary C.L., Alazab M. An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Computer Communications, 2020, vol. 160, pp. 139–149.https://doi.org/10.1016/j.comcom.2020.05.048
Khare N., Devan P., Chowdhary C.L., Bhattacharya S., Singh G., Singh S., Yoon B. SMO-DNN: Spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electronics, 2020,vol. 9. no. 4, pp. 692.https://doi.org/10.3390/electronics9040692

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License