1
Faculty of Artificial Intelligence, Egyptian Russian University (ERU), Cairo, Egypt
(Mahmoud.zaher@eru.edu.eg)
2
Faculty of Artificial Intelligence, Egyptian Russian University (ERU), Cairo, Egypt
(m.labeeb85@yahoo.com)
Abstract :
Wireless sensor networks (WSN) is widely utilized for collecting data related to physical parameters from the environment. Security remains a challenging issue in the design of WSN. Security in WSN from Denial of Service (DoS) attack is an important security risk. This study introduces an artificial flora optimization algorithm with functional link neural network (AFOA-FLNN) model for DoS attack classification in WSN. The presented AFOA-FLNN model initially undergoes data pre-processing to transform the data into meaningful way. Secondly, the FLNN model is utilized for the effective recognition and classification of intrusions in WSN. Finally, the AFOA is exploited for optimally tuning the parameters involved in the FLNN model and results in enhanced performance. In order to demonstrate the better outcomes of the AFOA-FLNN model, a wide-ranging experimentation assessment on test data and the results pointed out the improved outcomes of the AFOA-FLNN model.
Keywords :
DoS attack , Intrusion , Security , Machine learning , Parameter optimization , WSN
References :
[1] Arjunan, S. and Sujatha, P., 2018. Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Applied Intelligence, 48(8), pp.2229-2246.
[2] Arjunan, S. and Pothula, S., 2019. A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 31(3), pp.304-317.
[3] Arjunan, S., Pothula, S. and Ponnurangam, D., 2018. F5N‐based unequal clustering protocol (F5NUCP) for wireless sensor networks. International Journal of Communication Systems, 31(17), p.e3811.
[4] Famila, S., Jawahar, A., Sariga, A. and Shankar, K., 2020. Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments. Peer-to-Peer Networking and Applications, 13(4), pp.1071-1079.
[5] Premkumar, M. and Sundararajan, T.V.P., 2021. Defense countermeasures for DoS attacks in WSNs using deep radial basis networks. Wireless Personal Communications, 120(4), pp.2545-2560.
[6] Chen, H., Meng, C., Shan, Z., Fu, Z. and Bhargava, B.K., 2019. A novel Low-rate Denial of Service attack detection approach in ZigBee wireless sensor network by combining Hilbert-Huang Transformation and Trust Evaluation. IEEE Access, 7, pp.32853-32866.
[7] Almomani, I.M. and Alenezi, M., 2018. Efficient Denial of Service Attacks Detection in Wireless Sensor Networks. J. Inf. Sci. Eng., 34(4), pp.977-1000.
[8] Premkumar, M. and Sundararajan, T.V.P., 2020. DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks. Microprocessors and Microsystems, 79, p.103278.
[9] Islam, M.N.U., Fahmin, A., Hossain, M. and Atiquzzaman, M., 2021. Denial-of-service attacks on wireless sensor network and defense techniques. Wireless Personal Communications, 116(3), pp.1993-2021.
[10] Segura, G.A.N., Skaperas, S., Chorti, A., Mamatas, L. and Margi, C.B., 2020, June. Denial of service attacks detection in software-defined wireless sensor networks. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-7). IEEE.
[11] Yu, D., Kang, J. and Dong, J., 2021. Service attack improvement in wireless sensor network based on machine learning. Microprocessors and Microsystems, 80, p.103637.
[12] Ramesh, S., Yaashuwanth, C., Prathibanandhi, K., Basha, A.R. and Jayasankar, T., 2021. An optimized deep neural network based DoS attack detection in wireless video sensor network. Journal of Ambient Intelligence and Humanized Computing, pp.1-14.
[13] Ahmad, B., Jian, W., Enam, R.N. and Abbas, A., 2021. Classification of DoS attacks in smart underwater wireless sensor network. Wireless Personal Communications, 116(2), pp.1055-1069.
[14] Al-Ahmadi, S., 2021. Performance evaluation of machine learning techniques for DOS detection in wireless sensor network. International Journal of Network Security & Its Applications (IJNSA) Vol, 13.
[15] Katuwal, R. and Suganthan, P.N., 2019. Stacked autoencoder based deep random vector functional link neural network for classification. Applied Soft Computing, 85, p.105854.
[16] Naik, B., Obaidat, M.S., Nayak, J., Pelusi, D., Vijayakumar, P. and Islam, S.H., 2019. Intelligent secure ecosystem based on metaheuristic and functional link neural network for edge of things. IEEE Transactions on Industrial Informatics, 16(3), pp.1947-1956.
[17] Cheng, L., Wu, X.H. and Wang, Y., 2018. Artificial flora (AF) optimization algorithm. Applied Sciences, 8(3), p.329.
[18] Bacanin, N., Tuba, E., Bezdan, T., Strumberger, I. and Tuba, M., 2019, November. Artificial flora optimization algorithm for task scheduling in cloud computing environment. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 437-445). Springer, Cham.
[19] Wazirali, R., Ahmad, R. (2022). Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime. CMC-Computers, Materials & Continua, 70(3), 4922–4946.
| Style | # |
|---|---|
| MLA | Mahmoud A. Zaher, Mohmaed A. Labib. "Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN." International Journal of Wireless and Ad Hoc Communication, Vol. 4, No. 1, 2022 ,PP. 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |
| APA | Mahmoud A. Zaher, Mohmaed A. Labib. (2022). Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN. Journal of International Journal of Wireless and Ad Hoc Communication, 4 ( 1 ), 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |
| Chicago | Mahmoud A. Zaher, Mohmaed A. Labib. "Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN." Journal of International Journal of Wireless and Ad Hoc Communication, 4 no. 1 (2022): 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |
| Harvard | Mahmoud A. Zaher, Mohmaed A. Labib. (2022). Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN. Journal of International Journal of Wireless and Ad Hoc Communication, 4 ( 1 ), 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |
| Vancouver | Mahmoud A. Zaher, Mohmaed A. Labib. Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN. Journal of International Journal of Wireless and Ad Hoc Communication, (2022); 4 ( 1 ): 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |
| IEEE | Mahmoud A. Zaher, Mohmaed A. Labib, Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 4 , No. 1 , (2022) : 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101) |