• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 30, Pages: 3059-3069

Original Article

Bee inspired secured protocol for routing in cognitive radio ad hoc networks

Received Date:14 July 2020, Accepted Date:30 July 2020, Published Date:19 August 2020

Abstract

Background: Enhancing security and minimizing the delay is a significant task present in all modern networks. Dynamic emerging problems like node failure, route failure, intrusions, and security attacks lead the network to failure. Hence, there exist needs for development of a protocol that detects the intrusions and find the better route to destination. Objectives: The main objectives of this research is to understand the issues and challenges in routing that rise dynamically in cognitive radio ad hoc network and propose a bio-inspired routing protocol to enhance security and routing efficiency which will result in reduced delay cum energy consumption. Methods: This study proposes Bee Inspired Secured Protocol (BISP) for routing in cognitive radio ad hoc networks that focuses on increasing the security before sending data packets and decreasing the overall delay. An instinctive characteristic of Bees towards searching for food is utilized to design the proposed routing protocol, which selects the better path to the destination. To enrich the security during data transmission, Rivest Shamir Adelman algorithm is applied. The proposed protocol analyzes the security level of the route and neighbor node energy level before sending the data. Findings: NS2.35 simulator used to evaluate the performance of BISP. Simulation results indicate that BISP has better performance than the existing protocol (i.e., WPIP and GRP) in terms of throughput, packet delivery ratio, reduced delay and enhanced security. Novelty : Comprehensive analysis indicates that BISP have superior performance in classifying the intruding nodes, enhancing the security of data getting transmitted, and reducing the delay.

Keywords: Routing; security; intrusion; Bee; CRAHN

References

  1. Jihong W, Wenxiao S. Survey on cluster-based routing protocols for cognitive radio sensor networks. Journal on Communications. 2018;39:156–169. Available from: https://doi.org/10.11959/j.issn.1000-436x.2018244
  2. Manan J, Ahmed A, Ullah I, Merghem-Boulahia L, Gaïti D. Distributed intrusion detection scheme for next generation networks. Journal of Network and Computer Applications. 2019;147:102422. Available from: https://dx.doi.org/10.1016/j.jnca.2019.102422
  3. Viegas E, Santin A, Bessani A, Neves N. BigFlow: Real-time and reliable anomaly-based intrusion detection for high-speed networks. Future Generation Computer Systems. 2019;93:473–485. Available from: https://dx.doi.org/10.1016/j.future.2018.09.051
  4. Yang H, Qin G, Ye L. Combined Wireless Network Intrusion Detection Model Based on Deep Learning. IEEE Access. 2019;7:82624–82632. Available from: https://dx.doi.org/10.1109/access.2019.2923814
  5. Salo F, Injadat M, Nassif AB, Shami A, Essex A. Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review. IEEE Access. 2018;6:56046–56058. Available from: https://dx.doi.org/10.1109/access.2018.2872784
  6. Almogren AS. Intrusion detection in Edge-of-Things computing. Journal of Parallel and Distributed Computing. 2020;137:259–265. Available from: https://dx.doi.org/10.1016/j.jpdc.2019.12.008
  7. Lv L, Wang W, Zhang Z, Liu X. A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowledge-Based Systems. 2020;195:105648. Available from: https://dx.doi.org/10.1016/j.knosys.2020.105648
  8. Balakrishnan AN, Rajendran A, Pelusi D, Ponnusamy V. Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things. Internet of Things. 2019. Available from: https://doi.org/10.1016/j.iot.2019.100112 . Article ID 100112
  9. Yang A, Zhuansun Y, Liu C, Li J, Zhang C. Zhang Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network. IEEE Access. 2019;7:106043–106052. Available from: https://doi.org/10.1109/ACCESS.2019.2929919
  10. Lv S, Wang J, Yang Y, Liu J. Intrusion Prediction With System-Call Sequence-to-Sequence Model. IEEE Access. 2018;6:71413–71421. Available from: https://doi.org/10.1109/ACCESS.2018.2881561
  11. Prasad M, Tripathi S, Dahal K. An efficient feature selection based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing. 2020;87:105980. Available from: https://dx.doi.org/10.1016/j.asoc.2019.105980
  12. Mohammadi S, Mirvaziri H, Ghazizadeh-Ahsaee M, Karimipour H. Cyber intrusion detection by combined feature selection algorithm. Journal of Information Security and Applications. 2019;44:80–88. Available from: https://dx.doi.org/10.1016/j.jisa.2018.11.007
  13. Duan W, Tang X, Zhou J, Wang J, Zhou G. Load Balancing Opportunistic Routing for Cognitive Radio Ad Hoc Networks. Wireless Communications and Mobile Computing. 2018;2018:1–16. Available from: https://dx.doi.org/10.1155/2018/9412782
  14. Palanisamy R, V. M. BEE INSPIRED AGENT BASED ROUTING PROTOCOL-SECONDARY USER (BIABRP-SU) International Journal of Engineering and Technology. 2017;9(1):85–92. Available from: https://dx.doi.org/10.21817/ijet/2017/v9i1/170901407
  15. Srivastava A, Gupta MS, Kaur G. Energy efficient transmission trends towards future green cognitive radio networks (5G): Progress, taxonomy and open challenges. Journal of Network and Computer Applications. 2020;168:102760. Available from: https://dx.doi.org/10.1016/j.jnca.2020.102760
  16. Rivest RL, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM. 1978;21(2):120–126. Available from: https://dx.doi.org/10.1145/359340.359342
  17. Ramkumar J, Vadivel R. Performance modeling of bio-inspired routing protocols in Cognitive Radio Ad Hoc Network to reduce end-to-end delay”. International Journal of Intelligent Engineering and Systems. 2019;12:221–231. Available from: https://doi.org/10.22266/IJIES2019.0228.22
  18. Jin X, Zhang R, Sun J, Zhang Y. TIGHT: A geographic routing protocol for cognitive radio mobile Ad Hoc networks. IEEE Transactions on Wireless Communications. 2014;13:4670–4681. Available from: https://doi.org/10.1109/TWC.2014.2320950

Copyright

© 2020 Ramkumar & Vadivel.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).

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