Sinkhole Attack in IDS: Detection and Performance Analysis for Agriculture-based WSN using Cooja Network Simulator
Keywords:Sinkhole attack, IDS, Wireless Sensor Network, Cooja Network Simulator, WSN
Wireless Sensor Network (WSN) takes a major part in the world of technology and everyday lives by implementing Internet of Things (IoT) sensors into many kinds of environment such as agricultural area. Many farmers had applied WSN to help them to ease the tasks of tracking and collecting the important data status of their farms and greenhouses in order to maintain its ideal temperature, humidity and lights exposure. Despite these advantages, the WSN security issues could be questioned due to the possibility of being infected by the Distributed Denial of Service (DDoS) from the attackers. Sinkhole attack is the most common DDoS attack that happened in the agriculture environment WSN by sending a fake fastest route to the sensor nodes in the system. Therefore, this project has proposed an Intrusion Detection System (IDS) to detect the sinkhole attacks in the network of agriculture based WSN. The project ran with three simulations of network topologies to show the comparison based on the network traffic performance. The simulations consist of attack-free network, a network with sinkhole attack and IDS in the malicious sinkhole network. The main simulator for the study was Cooja Network Simulator, which was used to conduct all three simulations, while Wireshark was utilised to capture network traffic performance. For every simulation, 20 sensor nodes were implemented due to the facts that the number of the nodes are majorly and ideally used in real life environment of agriculture area. The findings showed that by implementing IDS in the agriculture WSN will gives better results in network traffic performances comparing to the attack-free network and sinkhole network without IDS. Thus, it proved that the proposed IDS could detect the network when uncommon behaviour appeared in the network topologies.
Ali, M., Nadeem, M., Siddique, A., Ahmad, S., & Ijaz, A. (2020). Addressing Sinkhole Attacks in Wireless Sensor Networks - A Review. International Journal of Scientific and Technology Research (IJSTR), 9(08).
Arora, S. K., Vijan, S., & Gaba, G. S. (2016). Detection and analysis of black hole attack using IDS. Indian Journal of Science and Technology, 9(20). Retrieved from https://doi.org/10.17485/ijst/2016/v9i20/85588
Deshmukh-Bhosale, S., & Sonavane, S. S. (2019). A Real-Time Intrusion Detection System for Wormhole Attack in the RPL based Internet of Things. In Procedia Manufacturing (Vol. 32, pp. 840â€“847). Elsevier B.V. https://doi.org/10.1016/j.promfg.2019.02.292
Lakshminarayana, D. H., Philips, J., & Tabrizi, N. (2019). A survey of intrusion detection techniques. In Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1122â€“1129). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICMLA.2019.00187
Stephen, R., & Arockiam, L. (2017). An Enhanced Technique to Detect Sinkhole Attack in Internet of Things. International Journal of Engineering Research & Technology (IJERT) ICONNECT â€“ 2017 (Volume 5 â€“ Issue 13). Retrieved from https://www.ijert.org/an-enhanced-technique-to-detect-sinkhole-attack-in-internet-of-things
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