CFP last date
15 January 2025
Call for Paper
February Edition
IJAIS solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 15 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Feature Selection based on Bat Algorithm and Residue Number System for Intrusion Detection System

by Bukola Fatimah Balogun, Kazeem Alagbe Gbolagade, Ayisat Wuraola Asaju- Gbolagade
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 39
Year of Publication: 2022
Authors: Bukola Fatimah Balogun, Kazeem Alagbe Gbolagade, Ayisat Wuraola Asaju- Gbolagade
10.5120/ijais2022451929

Bukola Fatimah Balogun, Kazeem Alagbe Gbolagade, Ayisat Wuraola Asaju- Gbolagade . Feature Selection based on Bat Algorithm and Residue Number System for Intrusion Detection System. International Journal of Applied Information Systems. 12, 39 ( June 2022), 32-37. DOI=10.5120/ijais2022451929

@article{ 10.5120/ijais2022451929,
author = { Bukola Fatimah Balogun, Kazeem Alagbe Gbolagade, Ayisat Wuraola Asaju- Gbolagade },
title = { Feature Selection based on Bat Algorithm and Residue Number System for Intrusion Detection System },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2022 },
volume = { 12 },
number = { 39 },
month = { June },
year = { 2022 },
issn = { 2249-0868 },
pages = { 32-37 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number39/1128-2022451929/ },
doi = { 10.5120/ijais2022451929 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:30+05:30
%A Bukola Fatimah Balogun
%A Kazeem Alagbe Gbolagade
%A Ayisat Wuraola Asaju- Gbolagade
%T Feature Selection based on Bat Algorithm and Residue Number System for Intrusion Detection System
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 39
%P 32-37
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet has grown rapidly in the last ten years. Consequently, the interconnection of computers and network devices has become so complex for monitoring that even the security experts do not fully understand its deepest inner workings. Personal computers have become very fast every year. It is not rare for a very ordinary person to connect to the Internet through 20 Mbs lines or faster. With this huge network data, the network security has become very important for monitoring the data. Machine Learning (ML) is a variant of Artificial Intelligence (AI) which uses algorithms to train and make accurate predictions on data. Dimensionality reduction in ML is used to remove redundant or irrelevant features, thereby improving the performance of classification. Chinese Remainder Theorem (CRT) is a modular arithmetic often used as a backward conversion algorithm in Residue Number System (RNS) to solve simultaneous linear congruence using a set of pair-wise relatively prime integers known as moduli set. In this paper, the Intrusion Detection System model was presented. The hybridized Bat algorithm and Chinese Remainder Theorem was used for feature selection and subsequently feature extraction was performed with Principal Component Analysis (PCA). The classification was done utilizing Naïve Bayes (NB). The dataset used for the experimental analysis was the Network Security Laboratory Knowledge Discovery Dataset (NSLKDD). In the experimental phase, 75% of the dataset was used for training and 25% for testing. The results obtained were measured in terms of accuracy, recall, sensitivity, specificity and precision.

References
  1. T. S. Sobh, “Wired and wireless intrusion detection system: Classifications, good characteristics and state-of-the-art,” Comput. Stand. Interfaces, vol. 28, no. 6, pp. 670–694, 2006, doi: 10.1016/j.csi.2005.07.002.
  2. P. Amudha, S. Karthik, and S. Sivakumari, “Classification Techniques for Intrusion Detection An Overview,” Int. J. Comput. Appl., vol. 76, no. 16, pp. 33–40, 2013, doi: 10.5120/13334-0928.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  3. E. Vasilomanolakis, S. Karuppayah, M. Muhlhauser, and M. Fischer, “Taxonomy and survey of collaborative intrusion detection,” ACM Comput. Surv., vol. 47, no. 4, pp. 1–33, 2015, doi: 10.1145/2716260.
  4. S. Sibi Chakkaravarthy, D. Sangeetha, and V. Vaidehi, “A Survey on malware analysis and mitigation techniques,” Comput. Sci. Rev., vol. 32, pp. 1–23, 2019, doi: 10.1016/j.cosrev.2019.01.002.
  5. J. Mchugh, A. Christie, and J.H. Allen,“ROle of Intrusion Detection Systems,” IEEE 17(5): 42 - 51, 2000, doi: 10.1109/52.877859.
  6. G. Hochman, A. Glöckner, and E. Yechiam, “Physiological measures in identifying decision strategies,” Found. Tracing Intuit. Challenges Methods, vol. 3, no. 3, pp. 139–159, 2009, doi: 10.4324/9780203861936.
  7. V. Manekar and K. Waghmare, “Intrusion Detection System using Support Vector Machine ( SVM ) and Particle Swarm Optimization ( PSO ),” no. 3, pp. 2–6, 2014.
  8. U. Lindqvist and E. Jonsson, “How to systematically classify computer security intrusions,” Doktors avhandlingar vid Chalmers Tek. Hogsk., no. 1530, pp. 83–99, 1999, doi: 10.1109/secpri.1997.601330.
  9. Wenke Lee, 1999 “A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems.” Columbia University, UMI Number: 9949009.
  10. M. K. Siddiqui and S. Naahid, “Analysis of KDD CUP 99 Dataset using Clustering based Data Mining,” Int. J. Database Theory Appl., vol. 6, no. 5, pp. 23–34, 2013, doi: 10.14257/ijdta.2013.6.5.03.
  11. S. J. Horng et al., “A novel intrusion detection system based on hierarchical clustering and support vector machines,” Expert Syst. Appl., vol. 38, no. 1, pp. 306–313, 2011, doi: 10.1016/j.eswa.2010.06.066.
  12. T. Eldos, “on the Kdd ’ 99 Dataset : Statistical Analysis for Feature Selection on the Kdd ’ 99 Dataset : Statistical Analysis for Feature Selection Taisir Eldos *, Mohammad Khubeb Siddiqui and Aws Kanan,” no. January 2012, 2014.
  13. I. Ahmad, M. Hussain, A. Alghamdi, and A. Alelaiwi, “Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components,” Neural Comput. Appl., vol. 24, no. 7–8, pp. 1671–1682, 2014, doi: 10.1007/s00521-013-1370-6.
  14. U. Ravale, N. Marathe, and P. Padiya, “Feature selection based hybrid anomaly intrusion detection system using K Means and RBF kernel function,” Procedia Comput. Sci., vol. 45, no. C, pp. 428–435, 2015, doi: 10.1016/j.procs.2015.03.174.
  15. Z. Dewa and L. A., “Data Mining and Intrusion Detection Systems,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 1, 2016, doi: 10.14569/ijacsa.2016.070109.
  16. A. Singh and A. Goyal, “Intrusion Detection System Based on Hybrid Optimization and using Neural Network: A Review,” Ijrece, vol. 6, no. 3, pp. 1138–1143, 2018, doi: 10.13140/RG.2.2.23285.83681.
  17. S. Chebrolu, A. Abraham, and J. P. Thomas, “Feature deduction and ensemble design of intrusion detection systems,” Comput. Secur., vol. 24, no. 4, pp. 295–307, 2005, doi: 10.1016/j.cose.2004.09.008.
  18. A. Jones and R. Sielken, “Computer system intrusion detection: A survey,” Comput. Sci. Tech. Rep., pp. 1–25, 2000.
  19. J. Raiyn, “A survey of cyber attack detection strategies,” Int. J. Secur. its Appl., vol. 8, no. 1, pp. 247–256, 2014, doi: 10.14257/ijsia.2014.8.1.23.
  20. M. Govindarajan and R. Chandrasekaran, “Intrusion detection using neural based hybrid classification methods,” Comput. Networks, vol. 55, no. 8, pp. 1662–1671, 2011, doi: 10.1016/j.comnet.2010.12.008.
  21. A. K. Ghosh, J. Wanken, and F. Charron, “Detecting anomalous and unknown intrusions against programs,” Proc. - Annu. Comput. Secur. Appl. Conf. ACSAC, pp. 259–267, 1998, doi: 10.1109/CSAC.1998.738646.
  22. X. S. Yang and A. H. Gandomi, “Bat algorithm: A novel approach for global engineering optimization,” Eng. Comput. (Swansea, Wales), vol. 29, no. 5, pp. 464–483, 2012, doi: 10.1108/02644401211235834.
  23. Y. Shen, K. Zheng, C. Wu, M. Zhang, X. Niu, and Y. Yang, “An Ensemble Method based on Selection Using Bat Algorithm for Intrusion Detection,” Comput. J., vol. 61, no. 4, pp. 526–538, 2018, doi: 10.1093/comjnl/bxx101.
  24. I. S. Volume, “Computing and Information Systems,” Inf. Syst., vol. 9, no. 2, 2005.
  25. C. H. Chang, A. S. Molahosseini, A. A. E. Zarandi, and T. F. Tay, “Residue number systems: A new paradigm to datapath optimization for low-power and high-performance digital signal processing applications,” IEEE Circuits Syst. Mag., vol. 15, no. 4, pp. 26–44, 2015, doi: 10.1109/MCAS.2015.2484118.
  26. K. J. Chabathula, C. D. Jaidhar, and M. A. Ajay Kumara, “Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms,” 2015 3rd Int. Conf. Signal Process. Commun. Networking, ICSCN 2015, pp. 1–6, 2015, doi: 10.1109/ICSCN.2015.7219853.
  27. J. Camacho, R. Theron, J. M. Garcia-Gimenez, G. MacIa-Fernandez, and P. Garcia-Teodoro, “Group-Wise Principal Component Analysis for Exploratory Intrusion Detection,” IEEE Access, vol. 7, pp. 113081–113093, 2019, doi: 10.1109/ACCESS.2019.2935154.
  28. L. Koc, T. A. Mazzuchi, and S. Sarkani, “A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier,” Expert Syst. Appl., vol. 39, no. 18, pp. 13492–13500, 2012, doi: 10.1016/j.eswa.2012.07.009.
  29. R. Kanagalakshmi and V. N. Raj, “Network Intrusion Detection Using Hidden Naive Bayes Multiclass Classifier Model,” no. 03, pp. 76–84, 2014.
  30. Soukaena Hassan Hashem, “Efficiency of SVM and PCA to Enhance Intrusion Detection System” vol. 3, no. 4, pp. 381–395, 2013.
  31. I. Ahmad, A. B. Abdullah, A. S. Alghamdi, M. Hussain, and K. Nafjan, “Features subset selection for network intrusion detection mechanism using genetic eigen vectors,” Proc. 2011 Int. Conf. Telecommun. Technol. Appl. (ICTTA 2011), vol. 5, no. November 2014, pp. 75–79, 2011.
  32. A. M. Hasan, M. Nasser, B. Pal, and S. Ahmad, “Intrusion Detection Using Combination of Various Kernels Based Support Vector Machine,” Int. J. Sci. Eng. Res., vol. 4, no. 9, pp. 1454–1463, 2013.
  33. Kavitha G., & Elango N.M (2020) "An Approach to Feature Selection in Intrusion Detection Systems Using Machine Learning Algorithms"International Journal of e- Collaboration 16(4) DOI: 10.4018/IJeC.2020100104.
  34. Q. R. S. Fitni and K. Ramli, "Implementation of Ensemble Learning and Feature Selection for Performance Improvements in Anomaly-Based Intrusion Detection Systems," 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2020, pp. 118-124, doi: 10.1109/IAICT50021.2020.9172014.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System Bat algorithm Residue Number System Principal Component Analysis Naïve Bayes

Powered by PhDFocusTM