Comparative Analysis of Intrusion Detection System in Reactive Routing Protocols of Mobile Adhoc Networks

##plugins.themes.academic_pro.article.main##

Manish Devendra Chawhan
Ausaf Umar Khan
Kishore Damodar Kulat
Bhumika Neole

Abstract

Mobile Nodes in Mobile Adhoc Network (MANET) interact wirelessly with the neighbor nodes without aid of
central management. They are subject to a variety of attacks, including as the black hole, insider, grey hole,
wormhole, flooding attack, and packet drop, all of which severely impair secure communication. This paper
incorporated an intrusion detection system (IDS) into a reactive MANET routing protocol, such as Adhoc OnDemand Distance Vector (AODV), for the detection and prevention of malicious nodes. To make a comparison
with the implemented IDSAODV, the existing NetSim code of Intrusion Detection System (IDS) based on Dynamic
Source Routing (DSR) protocol is utilized. We created a Black-hole node to conduct malicious activities in the
network. The IDSAODV and IDSDSR are analyzed for different Quality of Service (QoS) characteristics such
as Packet Delivery Ratio (PDR), Throughput, Energy Consumption, and delay in a network size of 10 nodes
for simulation period of 100 seconds. The NetSim tool was utilized as a simulation tool for creating network
with malicious node and to implement IDS. The results demonstrate that the IDSAODV and IDSDSR efficiently
identify and prevent a BH attack on the network. The IDSAODV improves PDR and throughput while consuming
more energy and having a little higher delay

##plugins.themes.academic_pro.article.details##

How to Cite
Chawhan, M. D., Khan, A. U., Kulat, K. D., & Neole, B. (2021). Comparative Analysis of Intrusion Detection System in Reactive Routing Protocols of Mobile Adhoc Networks. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.432

References

  1. Chawhan, M. D., Kulat, K. D., Pokle, S. B., and Khan, A. U. 2020. An advanced trust-based routing protocol for mobile adhoc network under black-hole attack. Bioscience Biotechnology Research Communications Vol. 13, No. 14, pp. 120-124.
  2. Datta, R. and Marchang, N. 2012. Security for mobile ad hoc networks. In Handbook on Securing Cyber-Physical Critical Infrastructure. pp.147-190.
  3. Deny, J., Kumar, A. S., Sundarajan, M., and Khanna, V. 2017. Defensive against collaborative attacks by malicious nodes in manets: A cooperative bait detection approach. In International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies. IEEE, pp.1-5.
  4. Fazeldehkordi, E. and Akanbi, O. 2015. A study of black hole attack solutions on aodv routing protocol in manet wireless network security. University Technology Malaysia Photonics Research Centre.
  5. Hurley-Smith, D., Wetherall, J., and Adekunle, A. 2017. Superman: Security using pre existing routing for mobile ad hoc networks. IEEE Transactions on Mobile Computing Vol.6, No.10, pp.2927-2940.
  6. Jhaveri, R., Desai, A., Patel, A., and Zhong, Y. 2018. A sequence number prediction based bait detection scheme to mitigate sequence number attacks in manets. Security and Communication Networks.
  7. Khan, A. U., Chawhan, M. D., Mushrif, M. M., and Neole, B. 2021. Performance analysis of adhoc on-demand distance vector protocol under the influence of black-hole, gray-hole and worm-hole attacks in mobile adhoc network. In 5th International Conference on Intelligent Computing and Control Systems. IEEE, pp.238-243.
  8. Khan, M. A., Nasralla, M. M., Umar, M. M., Iqbal, Z., Rehman, G. U., Sarfraz, M. S., and Choudhury, N. 2021. A survey on the noncooperative environment in smart nodes-based ad hoc networks: Motivations and solutions. Security and Communication Networks.
  9. Khan, M. S., Midi, D., Khan, M. I., and Bertino, E. 2017. Fine-grained analysis of packet loss in manets. IEEE Access Vol.5, pp. 7798-7807.
  10. Nadeem, A. and Howarth, M. P. 2013. A survey of manet intrusion detection and prevention approaches for network layer attacks. IEEE Communications Surveys and Tutorials Vol.15, No. 4, pp. 2027-2045.
  11. Saifuddin, K. M., Jobayer, A., Ali, B., Ahmed, A. S., Alam, S. S., and Ahmad, A. S. 2018. Watchdog and pathrater based intrusion detection system for manet. In 4th International Conference on Electrical Engineering and Information and Communication Technology. IEEE, pp.168-173.
  12. Talukdar, M. I., Hassan, R., Hossen, M. S., Ahmad, K., Qamar, F., and Ahmed, A. S. 2021. Performance improvements of aodv by black hole attack detection using ids and digital signature. Wireless Communications and Mobile Computing, pp. 1-13.
  13. Tsuda, T., Komai, Y., Hara, T., and Nishio, S. 2016. Top-k query processing and malicious node identification based on node grouping in manets. IEEE Access Vol. 4, pp. 993-1007.
  14. Xia, H., Li, Z., Zheng, Y., Liu, A., Choi, Y. J., and Sekiya, H. 2020. A novel lightweight subjective trust inference framework in manets. IEEE Transactions on Sustainable Computing Vol. 5, No. 2, pp. 236-248