Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection

I. O. Oyefolahan*, S. Idris**, J. N. Ndunagu***
* Department of Information Technology, School of ICT, Federal University of Technology, Minna, Nigeria.
** Department of Computer Science, School of ICT, Federal University of Technology, Minna, Nigeria.
*** Department of Computer Science, National Open University of Nigeria, Abuja, Nigeria.
Periodicity:March - May'2020
DOI : https://doi.org/10.26634/jcom.8.1.17459

Abstract

Intrusion detection system (IDS) like firewall, access control and encryption mechanisms no longer provide the much needed security for systems and computer networks. Current IDS are developed on anomaly detection which helps in identifying attacks both known and unknown. Unfortunately, these anomaly-based IDS features high false rate. In a bid to reduce this false alarm rate, this paper proposed an intrusion detection model based on Support Vector Machine (SVM) optimized with Cat swarm optimization (CSO) algorithm. Attribute reduction has been carried out based on Information Gain (IG) and classification has been performed based on the optimized Support vector. The result obtained shows that our model performs well with the least false alarm rate and good accuracy value compared with other classification algorithms evaluated using the same datasets.

Keywords

Intrusion Detection, Support Vector Machine, Cat Swarm Optimization, Information Gain, NSL-KDD.

How to Cite this Article?

Oyefolahan, O. I., Idris, S., and Ndunagu, J. N.(2020). Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection. i-manager's Journal on Computer Science, 8(1), 1-13. https://doi.org/10.26634/jcom.8.1.17459

References

[1]. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152-160. https://doi.org/10.1016/j.jocs.2017.03.006
[2]. Al-Yaseen, W. L., Othman, Z. A., & Nazri, M. Z. A. (2017). Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Systems with Applications, 67, 296-303. https://doi.org/10.1016/j.eswa.2016.09.041
[3]. Amami, R., Ayed, D. B., & Ellouze, N. (2015). Practical selection of SVM supervised parameters with different feature representations for vowel recognition. International Journal of Digital Content Technology and its Applications, 7(9), 418-424.
[4]. Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas, H., & He, Y. L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484-497. https://doi.org/10.1016/j.ins.20 16.04.019
[5]. Bahrami, M., Bozorg-Haddad, O., & Chu, X. (2018). Cat swarm optimization (CSO) algorithm. In Advanced Optimization by Nature-Inspired Algorithms (pp. 9-18). Singapore: Springer. https://doi.org/10.1007/978-981-10- 5221-7_2
[6]. DaÅŸ, R., Karabade, A., & Tuna, G. (2015, May). Common network attack types and defense mechanisms. In 2015 23rd Signal Processing and Communications Applications Conference (SIU) (pp. 2658-2661). IEEE. https://doi.org/10.1109/SIU.2015.7130435
[7]. Desai, S. P., Hadule, P. R., & Dudhgaonkar, A. A. (2017). Denial of service attack defense techniques. International Research Journal of Engineering and Technology (IRJET), 4(10), 1532 – 1535.
[8]. Devare, A., Shelake, M., Vahadne, V., Kamble, P., & Tamboli, B. (2016). A system for denial-of-service attack detection based on multivariate correlation analysis. International Research Journal of Engineering and Technology (IRJET), 3(04). 1917 – 1923.
[9]. Dhanabal, L., & Shantharajah, S. P. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 446-452.
[10]. Enache, A. C., & Patriciu, V. V. (2014, May). Intrusions detection based on support vector machine optimized with swarm intelligence. In 2014 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) (pp. 153-158). IEEE. https://doi.org/10. 1109/SACI.2014.6840052
[11]. Hadi, I., & Sabah, M. (2015). Improvement cat swarm optimization for efficient motion estimation. International Journal of Hybrid Information Technology, 8(1), 279-294. https://doi.org/10.14257/ijhit.2015.8.1.25
[12]. Harshita, H. (2017). Detection and prevention of ICMP flood DDOS attack. International Journal of New Technology and Research, 3(3), 63-69.
[13]. Hassan, A. A., Sheta, A. F., & Wahbi, T. M. (2017). Intrusion detection system using Weka data mining tool. International Journal of Science and Research, 6(9), 337 – 342.
[14]. Hoque, N., Kashyap, H., & Bhattacharyya, D. K. (2017). Real-time DDoS attack detection using FPGA. Computer Communications, 110, 48-58. https://doi.org /10.1016/j.comcom.2017.05.015
[15]. Kevric, J., Jukic, S., & Subasi, A. (2017). An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Computing and Applications, 28(1), 1051-1058. https://doi.org/10. 1007/s00521-016-2418-1
[16]. Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing, 18, 178-184. https://doi.org/10. 1016/j.asoc.2014.01.028
[17]. Kumar, A., Maurya, H. C., & Misra, R. (2013). A research paper on hybrid intrusion detection system. International Journal of Engineering and Advanced Technology (IJEAT), 2(4), 294-297.
[18]. Kumar, M., Mishra, S. K., & Sahu, S. S. (2016). Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images. Applied Computational Intelligence and Soft Computing, 2016, 6. https://doi.org/10.1155/201 6/6304915
[19]. Lin, K. C., Zhang, K. Y., Huang, Y. H., Hung, J. C., & Yen, N. (2016). Feature selection based on an improved cat swarm optimization algorithm for big data classification. The Journal of Supercomputing, 72(8), 3210-3221. https://doi.org/10.1007/s11227-016-1631-0
[20]. Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-based Systems, 78, 13-21. https://doi.org/10.1016/j.knosys.2015 .01.009
[21]. Mahjabin, T., Xiao, Y., Sun, G., & Jiang, W. (2017). A survey of distributed denial-of-service attack, prevention, and mitigation techniques. International Journal of Distributed Sensor Networks, 13(12), 1-33. https://doi.org/ 10.1177%2F1550147717741463
[22]. Nidhi, M. V., & Prasad, K. M. (2016). Detection of anomaly based application layer DDoS attacks using machine learning approaches. i-manager's Journal on Computer Science, 4(2), 6-13.
[23]. Raiyn, J. (2014). A survey of cyber attack detection strategies. International Journal of Security and its Applications, 8(1), 247-256. https://doi.org/10.14257/ijsia.2 014.8.1.23
[24]. Saied, A., Overill, R. E., & Radzik, T. (2016). Detection of known and unknown DDoS attacks using Artificial Neural Networks. Neurocomputing, 172, 385-393. https://doi.org/ 10.1016/j.neucom.2015.04.101
[25]. Singh, K. J., & De, T. (2017). MLP-GA based algorithm to detect application layer DDoS attack. Journal of Information Security and Applications, 36, 145-153. https://doi.org/10.1016/j.jisa.2017.09.004
[26]. Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2013). A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Transactions on Parallel and Distributed Systems, 25(2), 447-456. https://doi.org/10.1109/TPDS.2013.146
[27]. Yadav, V. K., Trivedi, M. C., & Mehtre, B. M. (2016). DDA: an approach to handle DDoS (Ping flood) attack. In Proceedings of International Conference on ICT for Sustainable Development (pp. 11-23). Singapore: Springer. https://doi.org/10.1007/978-981-10-0129-1_2
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.