International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Expedient Intrusion Detection System in MANET Using Robust Dragonfly-Optimized Enhanced Naive Bayes (RDO-ENB)

Author NameAuthor Details

M. Sasikumar, K. Rohini

M. Sasikumar[1]

K. Rohini[2]

[1]School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India.

[2]Department of Information Technology, School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India.

Cite this Article

DOI: 10.22247/ijcna/2024/224435

Pages : 46-60

 Download PDF

987

Views

Abstract

Mobile Ad hoc networks (MANETs) represent dynamic, self-configuring network environments that provide flexible connectivity but are highly susceptible to security threats. Intrusion detection systems in MANETs need to continuously monitor network traffic for potential intrusions and anomalies. This constant monitoring can be energy-intensive, requiring network nodes to process, analyze, and transmit data. Excessive energy consumption by IDS can deplete node batteries quickly, leading to network disruptions. This research focuses on developing and evaluating an efficient IDS proposed for MANETs called Robust Dragonfly-Optimized Naive Bayes (RDO-ENB). RDO-ENB operates by fusing the simplicity and efficiency of the Enhanced Naive Bayes algorithm with the adaptive capabilities of robust Dragonfly Optimization. This synergy enables RDO-ENB to continuously and dynamically adjust its internal parameters, optimizing its intrusion detection performance in real time. It enhances accuracy and reduces false positives, making it proficient in identifying and mitigating intrusions within the complex and ever-evolving environment of MANETs. The dataset employed for evaluation is NSL-KDD, a widely used dataset for intrusion detection. The results of the IDS implementation demonstrate its proficiency in accurately identifying and mitigating intrusions while minimizing false positives and conserving valuable energy resources.

Index Terms

Dragonfly

Naive Bayes

Intrusion

MANET

Classification

Chaos

Reference

  1. 1.
    B. J. Chang, Y. H. Liang, and Y. M. Lin, “Distributed route repair for increasing reliability and reducing control overhead for multicasting in wireless MANET,” Inf. Sci. (Ny)., vol. 179, no. 11, pp. 1705–1723, 2009, doi: 10.1016/j.ins.2009.01.013.
  2. 2.
    Z. A. Younis, A. M. Abdulazeez, S. S. R. M. Zeebaree, R. R. Zebari, and D. Q. Zeebaree, “Mobile Ad Hoc Network in Disaster Area Network Scenario; A Review on Routing Protocols,” Int. J. online Biomed. Eng., vol. 17, no. 3, pp. 49–75, 2021, doi: 10.3991/ijoe.v17i03.16039.
  3. 3.
    M. U. Farooq and M. Zeeshan, “Connected dominating set enabled on-demand routing (CDS-OR) for wireless mesh networks,” IEEE Wirel. Commun. Lett., vol. 10, no. 11, pp. 2393–2397, 2021, doi: 10.1109/LWC.2021.3101476.
  4. 4.
    R. J. Shimonski, W. Schmied, T. W. Shinder, V. Chang, D. Simonis, and D. Imperatore, “DMZ Router and Switch Security,” in Building DMZs For Enterprise Networks, R. J. Shimonski, W. Schmied, T. W. Shinder, V. Chang, D. Simonis, and D. B. T.-B. Dmz. F. E. N. Imperatore, Eds., Rockland: Syngress, 2003, pp. 369–430. doi: 10.1016/b978-193183688-3/50012-2.
  5. 5.
    U. Srilakshmi, S. A. Alghamdi, V. A. Vuyyuru, N. Veeraiah, and Y. Alotaibi, “A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks,” IEEE Access, vol. 10, pp. 14260–14269, 2022, doi: 10.1109/ACCESS.2022.3144679.
  6. 6.
    I. Martins, J. S. Resende, P. R. Sousa, S. Silva, L. Antunes, and J. Gama, “Host-based IDS: A review and open issues of an anomaly detection system in IoT,” Futur. Gener. Comput. Syst., vol. 133, pp. 95–113, 2022, doi: 10.1016/j.future.2022.03.001.
  7. 7.
    S. N. G. Aryavalli and H. Kumar, “Top 12 layer-wise security challenges and a secure architectural solution for Internet of Things,” Comput. Electr. Eng., vol. 105, p. 108487, 2023, doi: 10.1016/j.compeleceng.2022.108487.
  8. 8.
    A. Chourasia and A. Namdev, “Improved wireless mobile ad hoc network using security schemes against black-hole attack,” Int. J. Emerg. Technol. Adv. Eng., vol. 10, no. 11, pp. 61–69, 2020, doi: 10.46338/ijetae1120_07.
  9. 9.
    S. Sargunavathi and J. Martin Leo Manickam, “Enhanced trust based encroachment discovery system for Mobile Ad-hoc networks,” Cluster Comput., vol. 22, pp. 4837–4847, 2019, doi: 10.1007/s10586-018-2405-7.
  10. 10.
    S. Gopinath, N. A. Natraj, D. Bhanu, and N. Sureshkumar, “Reliability integrated intrusion detection system for isolating black hole attack in MANET,” J. Sci. Ind. Res. (India)., vol. 79, no. 10, pp. 905–908, 2020, doi: 10.56042/jsir.v79i10.43535.
  11. 11.
    N. Rajendran, P. K. Jawahar, and R. Priyadarshini, “Makespan of routing and security in Cross Centric Intrusion Detection System (CCIDS) over black hole attacks and rushing attacks in MANET,” Int. J. Intell. Unmanned Syst., vol. 7, no. 4, pp. 162–176, 2019, doi: 10.1108/IJIUS-03-2019-0021.
  12. 12.
    M. Prasad, S. Tripathi, and K. Dahal, “An intelligent intrusion detection and performance reliability evaluation mechanism in mobile ad-hoc networks,” Eng. Appl. Artif. Intell., vol. 119, 2023, doi: 10.1016/j.engappai.2022.105760.
  13. 13.
    E. A. Shams, A. Rizaner, and A. H. Ulusoy, “Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction,” Veh. Commun., vol. 41, p. 100585, 2023, doi: 10.1016/j.vehcom.2023.100585.
  14. 14.
    R. P. P and S. shankar, “Secure intrusion detection system routing protocol for mobile ad?hoc network,” Glob. Transitions Proc., vol. 3, no. 2, pp. 399–411, 2022, doi: 10.1016/j.gltp.2021.10.003.
  15. 15.
    S. B. Ninu, “An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET,” Eng. Appl. Artif. Intell., vol. 123, p. 105969, 2023, doi: 10.1016/j.engappai.2023.105969.
  16. 16.
    M. kumar Pulligilla and C. Vanmathi, “An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection,” Internet of Things (Netherlands), vol. 22, p. 100723, 2023, doi: 10.1016/j.iot.2023.100723.
  17. 17.
    N. Omer, A. H. Samak, A. I. Taloba, and R. M. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization,” Alexandria Eng. J., vol. 72, pp. 351–361, 2023, doi: 10.1016/j.aej.2023.03.093.
  18. 18.
    A. Mabrouk and A. Naja, “Intrusion detection game for ubiquitous security in vehicular networks: A signaling game based approach,” Comput. Networks, vol. 225, p. 109649, 2023, doi: 10.1016/j.comnet.2023.109649.
  19. 19.
    J. L. Webber et al., “An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks,” Comput. Electr. Eng., vol. 111, p. 108964, 2023, doi: 10.1016/j.compeleceng.2023.108964.
  20. 20.
    S. Ullah et al., “TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks,” Comput. Networks, vol. 237, p. 110072, 2023, doi: 10.1016/j.comnet.2023.110072.
  21. 21.
    M. V. B. M. K. M, C. A. Ananth, and N. Krishnaraj, “Detection of intrusions in clustered vehicle networks using invasive weed optimization using a deep wavelet neural networks,” Meas. Sensors, vol. 28, p. 100807, 2023, doi: 10.1016/j.measen.2023.100807.
  22. 22.
    M. V. Rajesh, “Intensive analysis of intrusion detection methodology over Mobile Adhoc Network using machine learning strategies,” Mater. Today Proc., vol. 51, pp. 156–160, 2021, doi: 10.1016/j.matpr.2021.05.066.
  23. 23.
    F. S. Al-Anzi, “Design and analysis of intrusion detection systems for wireless mesh networks,” Digit. Commun. Networks, vol. 8, no. 6, pp. 1068–1076, 2022, doi: 10.1016/j.dcan.2022.05.013.
  24. 24.
    J. Ramkumar, S. S. Dinakaran, M. Lingaraj, S. Boopalan, and B. Narasimhan, “IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion,” in Lecture Notes in Electrical Engineering, K. Murari, N. Prasad Padhy, and S. Kamalasadan, Eds., Singapore: Springer Nature Singapore, 2023, pp. 17–27. doi: 10.1007/978-981-19-8353-5_2.
  25. 25.
    R. Jaganathan, V. Ramasamy, L. Mani, and N. Balakrishnan, “Diligence Eagle Optimization Protocol for Secure Routing (DEOPSR) in Cloud-Based Wireless Sensor Network,” 2022, doi: 10.21203/rs.3.rs-1759040/v1.
  26. 26.
    L. Mani, S. Arumugam, and R. Jaganathan, “Performance Enhancement of Wireless Sensor Network Using Feisty Particle Swarm Optimization Protocol,” ACM Int. Conf. Proceeding Ser., pp. 1–5, Dec. 2022, doi: 10.1145/3590837.3590907.
  27. 27.
    S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073, 2016, doi: 10.1007/s00521-015-1920-1.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions