Intelligent Knowledge Exploration and Processing

Intelligent Knowledge Exploration and Processing

Optimum increase of lifetime of wireless sensor network after smurf attack with system utilization Network-based intrusion detection and K-MEAM clustering algorithm

Document Type : Original Article

Authors
Ali Ezzatie 1
mohammad mahdi shirmohammadi 2
1 MSc. Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
2 Professor, Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
10.30508/kdip.2024.469585.1108
Abstract
In recent decades, wireless sensor networks have faced problems. One of the main problems is security. Subsequent advances in network connectivity and computing capabilities have expanded the applications of wireless sensor networks (WSNs). Data collection and transmission to a remote server, located in isolated locations, is the main goal of wireless sensor networks (WSNs). These networks may be underground, underwater, terrestrial or multimodal. Among them are industrial automation, traffic management, monitoring of medical devices and other areas. Despite the growth of the market, wireless sensor networks are facing challenges. Energy efficiency, manufacturing and processing boundaries, bandwidth, error rate, stored resources and survival under harsh conditions should be considered. Using some network security methods, it is possible to deal with the target, but these methods have a negative effect on the lifetime of network sensors and cause a loss of overall network energy. In this article, by simulating a wireless sensor network, we first use the k-means clustering algorithm to divide the nodes into different clusters in terms of their distance from the other nodes and cluster heads, as well as the energy consumption of each node for Sending messages and the distance from the base station in the most optimal possible state. Then, by simulating the Smurf attack, several nodes send too many messages, which causes the energy consumption of the node, then by creating an intrusion detection system, the attacking nodes are identified and removed. The innovation of this article is that in addition to identifying other attacking nodes whose status is not normal, it is identified and considered as suspicious nodes, and in the end, it increases the overall lifespan of the collection by removing the attacking nodes.
Keywords
Ka-min algorithm
smurf attack
clustering
network-based intrusion detection system
wireless sensor network
1.       Chen, B., Hu, J., Zhao, Y., & Ghosh, B. K. (2022). Finite-time velocity-free rendezvous control of multiple AUV systems with intermittent communication. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(10), 6618-6629.
2.       Cheng, B., Zhu, D., Zhao, S., & Chen, J. (2016). Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Transactions on Network and Service Management, 13(2), 349-361.
3.       Fang, Y., Min, H., Wu, X., Wang, W., Zhao, X., & Mao, G. (2022). On-ramp merging strategies of connected and automated vehicles considering communication delay. IEEE Transactions on Intelligent Transportation Systems, 23(9), 15298-15312.
4.       Han, Y., Wang, B., Guan, T., Tian, D., Yang, G., Wei, W., ... & Chuah, J. H. (2022). Research on road environmental sense method of intelligent vehicle based on tracking check. IEEE transactions on intelligent transportation systems, 24(1), 1261-1275.
5.       Ifzarne, S., Tabbaa, H., Hafidi, I., & Lamghari, N. (2021). Anomaly detection using machine learning techniques in wireless sensor networks. In Journal of Physics: Conference Series (Vol. 1743, No. 1, p. 012021). IOP Publishing.
6.       Jiang, S., Zhao, C., Zhu, Y., Wang, C., & Du, Y. (2022). A Practical and Economical Ultra‐wideband Base Station Placement Approach for Indoor Autonomous Driving Systems. Journal of advanced transportation, 2022(1), 3815306.
7.       Jiang, Y., Liu, S., Li, M., Zhao, N., & Wu, M. (2022). A new adaptive co-site broadband interference cancellation method with auxiliary channel. Digital Communications and Networks.
8.      Liu, G. (2023). A Q-Learning-based distributed routing protocol for frequency-switchable magnetic induction-based wireless underground sensor networks. Future Generation Computer Systems, 139, 253-266.
9.       Liu, X., Shi, T., Zhou, G., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023). Emotion classification for short texts: an improved multi-label method. Humanities and Social Sciences Communications, 10(1), 1-9.
10.   Lv, Z., Cheng, C., & Song, H. (2022). Digital twins based on quantum networking. Ieee Network, 36(5), 88-93.
11.   Lv, Z., Qiao, L., & Nowak, R. (2022). Energy-efficient resource allocation of wireless energy transfer for the internet of everything in digital twins. IEEE Communications Magazine, 60(8), 68-73.
12.   Pandey, A., Kumar, D., Priyadarshi, R., & Nath, V. (2022). Development of smart village for better lifestyle of farmers by crop and health monitoring system. In Microelectronics, Communication Systems, Machine Learning and Internet of Things: Select Proceedings of MCMI 2020 (pp. 689-694). Singapore: Springer Nature Singapore.
13.   Sateesh, V. A., Dutta, I., Priyadarshi, R., & Nath, V. (2021). Fractional frequency reuse scheme for noise-limited cellular networks. In Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems: MCCS 2019 (pp. 995-1004). Springer Singapore.
14.   Sen, S., Sahoo, L., Tiwary, K., Simic, V., & Senapati, T. (2023). Wireless sensor network lifetime extension via K-Medoids and MCDM techniques in uncertain environment. Applied Sciences, 13(5), 3196.
15.   Xu, K. D., Guo, Y. J., Liu, Y., Deng, X., Chen, Q., & Ma, Z. (2021). 60-GHz compact dual-mode on-chip bandpass filter using GaAs technology. IEEE Electron Device Letters, 42(8), 1120-1123.
16.   Xiao, Z., Li, H., Jiang, H., Li, Y., Alazab, M., Zhu, Y., & Dustdar, S. (2023). Predicting urban region heat via learning arrive-stay-leave behaviors of private cars. IEEE transactions on intelligent transportation systems, 24(10), 10843-10856.
17.   Zhou, G., Zhang, R., & Huang, S. (2021). Generalized buffering algorithm. IEEE access, 9, 27140-27157.
 
Intelligent Knowledge Exploration and Processing
Volume 4, Issue 13
Summer 2024
Pages 52-59