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