Predicting Malicious Node Behavior in Wireless Network Using DSR Protocol and Network Metrics

  • Ganesan A Department of Computer Applications, Hindusthan Arts College, Coimbatore–641028, Tamil Nadu, India.
  • Kumar Kombaiya A Department of Computer Science, Chikkanna Government Arts College, Tirupur-641602, Tamil Nadu, India.
Keywords: Malicious Node, DSR, Throughput, Latency, NS2, Packet Delivery Ratio, Energy Consumption, Intrusion Detection System

Abstract

This paper describes a set of network metrics are helpful to predict behavior of malicious node in wireless network. The Network and internet is the device in which multiple people can communicate with each other through the wired or wireless media. Nowadays, Internet of Things, Mobile, vehicular, and wireless ad hoc networks all merge into one shared network. These networks are often used to send receive confidential data and information. The unauthorized or malicious node misuse these secrete information. With a rise in rogue nodes, network performance will suffer. A rogue node in the network can cause variations in network metrics including the packet dropping percentage, throughput, latency, energy consumption, and average queue duration. This behavior used to identify the malicious node.

Metrics

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References

The Network Simulator - ns-2. https://www.isi.edu/nsnam/ns/index.html

Fall K., & Varadhan K, (2011). The ns Manual (formerly ns Notes and Documentation), https://www.isi.edu/nsnam/ns/doc/ns_doc.pdf

Chung, J., & Claypool, M. (2002). NS by example. Worcester Polytechnic Institute, Computer Science. http://nile.wpi.edu/NS/

Wei, D.X., (2005). Speeding up NS-2 scheduler. http://netlab.caltech.edu/projects/ns2tcplinux/ns2patch/ns2patch.htm

Das, S.R., Castaneda, R., Yan, J., & Sengupta, R., (1998). Comparative performance evaluation of routing protocols for mobile, ad hoc networks, In Proceedings 7th International Conference on Computer Communications and Networks, IEEE, 153- 161. https://doi.org/10.1109/ICCCN.1998.998772

Sahu, P., Bisoy, S.K., & Sahoo, S., (2013). Detecting and isolating malicious node in AODV routing algorithm, International Journal of Computer Applications, 66 (16), 8- 12.

Khandakar, A., (2012). Step by step procedural comparison of DSR, AODV and DSDV routing protocol, 4th In International Proceedings of Computer Science &Information Techenology, 40 (12), 36-40.

Baliga, J., Ayre, R., Hinton, K., & Tucker, R.S., (2011). Energy consumption in wired and wireless access networks, IEEE Communications Magazine, 49(6), 70-77. https://doi.org/10.1109/MCOM.2011.5783987

Rohini, R., & Gnanamurthy, R.K., (2016). A Simple and Efficient Malicious node detection system for improving the performance of the wireless sensor networks, International Journal of Applied Engineering Research, 11(1), 396-400.

Singh, R., Singh, J., & Singh, R. (2017). Fuzzy based advanced hybrid intrusion detection systemtodetect maliciousnodes inwirelesssensor networks, Wireless Communications and Mobile Computing, 2017, 1-14. https://doi.org/10.1155/2017/3548607

Sarigiannidis, P., Karapistoli, E., & Economides, A.A., (2015). Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information, Expert Systems with Applications, 42 (21), 7560-7572. https://doi.org/10.1016/j.eswa.2015.05.057

Maleh, Y., Ezzati, A., Qasmaoui, Y., & Mbida, M., (2015). A global hybrid intrusion detection system for wireless sensor networks, Procedia Computer Science, 52, 1047- 1052. https://doi.org/10.1016/j.procs.2015.05.108

Adnan, A., Kamalrulnizam, A.B., Muhammad Ibrahim, C., Khalid, H., & Abdul Waheed, K., (2014) A Survey on Trust-Based Detection and Isolation of Malicious Nodes in Ad-Hoc and Sensor Networks, Frontiers of Computer Science (electronic), 9(2), 280-296. http://dx.doi.org/10.1007/s11704-014-4212-5

Li, Z., Sun, J., Yan, Q., Srisa-an, W., & Bachala, S., (2018). Grandroid: Graph-based detection of malicious network behaviors in android applications, In International Conferenceon Securityand Privacy in Communication Systems, Springer, Cham, 254, 264-280. https://doi.org/10.1007/978-3-030-01701-9_15

Rajasekaran, B., & Arun, C., (2018). Detection of malicious nodes in wireless sensor networks based on features using neural network computing approach, International Journal of Recent Technology and Engineering, 7(4), 188-192.

Published
2022-05-03
How to Cite
A, G., & A, K. K. (2022). Predicting Malicious Node Behavior in Wireless Network Using DSR Protocol and Network Metrics. International Journal of Computer Communication and Informatics, 4(1), 1-10. https://doi.org/10.34256/ijcci2211



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