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FSSAM: A Five Stage Security Analysis Model for Detecting and Preventing Wormhole Attack in Mobile Ad-Hoc Networks Using Adaptive Atom Search Algorithm

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Abstract

Mobile Ad-hoc Network is a decentralized wireless network which is drastically growing day by day. It can be vulnerable to several threats and attacks, namely wormhole attacks, due to some characteristics, such as node, location, time etc.; hence it leads to loss of data, route failure, and data route diversion. Moreover, several existing approaches have been introduced to resolve the wormhole attack, yet suffering from higher consumed energy, high delay in packet delivery and less throughput. A Five Stage Security Analysis Model is developed to effectively detect and prevent the wormhole attack by considering these problems. The five-stage model investigates the information like route, node location, time, hop count and neighbour analysis in the network. The proposed model cannot need any middleware or some special type of hardware, and also it minimizes the energy and delay by keeping away from executing wormhole detection for every node present in the network. The Network Simulator-2 software is used to provide the simulation of the proposed model FSSAM. The performance is evaluated, and simulation results are verified in terms of throughput, delay, energy, packet loss, end-to-end delay and packet delivery ratio. The determined outcomes have proved that the proposed model performed better for wormhole detection and prevention than existing approaches.

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References

  1. Tahboush, M., & Agoyi, M. (2021). A hybrid wormhole attack detection in mobile ad-hoc network (MANET). IEEE Access, 9, 11872–11883.

    Article  Google Scholar 

  2. Liu, G., Yan, Z., & Pedrycz, W. (2018). Data collection for attack detection and security measurement in mobile ad hoc networks: A survey. Journal of Network and Computer Applications, 105, 105–122.

    Article  Google Scholar 

  3. Quy, V. K., Ban, N. T., Nam, V. H., Tuan, D. M., & Han, N. D. (2019). Survey of recent routing metrics and protocols for mobile ad-hoc networks. The Journal of Communication, 14(2), 110–120.

    Article  Google Scholar 

  4. Majumder, S., & Bhattacharyya, D. (2018). Mitigating wormhole attack in MANET using absolute deviation statistical approach. In Proceedings of the IEEE 8th annual computer communication workshop conference. (CCWC), Las Vegas (pp. 317–320).

  5. Ali, S., Nand, P., & Tiwari, S. (2022). Detection of wormhole attack in vehicular ad-hoc network over real map using machine learning approach with preventive scheme. Journal of Information Technology Management, 14(Security and Resource Management challenges for Internet of Things), 159–179.

    Google Scholar 

  6. Abdan, M., & Seno, S. A. H. (2022). Machine learning methods for intrusive detection of wormhole attack in mobile ad-hoc network (MANET).

  7. Alenezi, F. A., Song, S., & Choi, B. Y. (2021). SWANS: SDN-based Wormhole Analysis using the neighbor similarity for a mobile ad hoc network (MANET). In 2021 IFIP/IEEE international symposium on integrated network management (IM) (pp. 653–657).

  8. Umoren, I., Okpongkpong, E., & Udoeka, I. (2021). A fuzzy knowledge-based approach for end-2-end behavioural analysis of wormhole attacks on mobile ad-hoc networks. International Journal of Information Systems and Informatics, 2(4), 1–17.

  9. Mirza, S., & Bakshi, S. Z. (2018). Introduction to MANET. International research journal of engineering and technology, 5(1), 17–20.

    Google Scholar 

  10. Amutha, S., & Balasubramanian, K. (2018). Secured energy optimized ad hoc on-demand distance vector routing protocol. Computers and Electrical Engineering, 72, 766–773.

    Article  Google Scholar 

  11. Rezaei, S., Gharib, M., & Movaghar, A. (2018). Throughput analysis of IEEE 80211 multi-hop wireless networks with routing consideration: A general framework. IEEE Transactions on Communications, 66(11), 5430–5443.

    Article  Google Scholar 

  12. Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 20(4), 2595–2621.

    Article  Google Scholar 

  13. Rath, M., Swain, J., Pati, B., & Pattanayak, B. K. (2018). Network security: Attacks and control in MANET. In Handbook of research on network forensics and analysis techniques, IGI Global (pp. 19–37).

  14. Perazzo, P., Vallati, C., Varano, D., Anastasi, G., & Dini, G. (2018). Implementation of a wormhole attack against a rpl network: Challenges and effects. In 2018 14th Annual conference on wireless on-demand network systems and services (WONS), IEEE (pp. 95–102).

  15. Prasad, M., Tripathi, S., & Dahal, K. (2022). An enhanced detection system against routing attacks in mobile ad-hoc network. Wireless Networks, 28(4), 1411–1428.

    Article  Google Scholar 

  16. Majumder, S., & Bhattacharyya, D. (2020). Relation estimation of packets dropped by wormhole attack to packets sent using regression analysis. In Emerging technology in modelling and graphics (pp. 557–566). Springer, Singapore.

  17. Saudi, N. A. M., Arshad, M. A., Buja, A. G., Fadzil, A. F. A., & Saidi, R. M. (2019). Mobile ad-hoc network (MANET) routing protocols: A performance assessment. In Proceedings of the third international conference on computing, mathematics and statistics (iCMS2017) (pp. 53–59). Springer, Singapore.

  18. Krishnan, R. S., Julie, E. G., Robinson, Y. H., Kumar, R., Tuan, T. A., & Long, H. V. (2020). Modified zone based intrusion detection system for security enhancement in mobile ad hoc networks. Wireless Networks, 26(2), 1275–1289.

    Article  Google Scholar 

  19. Vo, T. T., Luong, N. T., & Hoang, D. (2019). MLAMAN: A novel multi-level authentication model and protocol for preventing wormhole attack in mobile ad hoc network. Wireless Networks, 25(7), 4115–4132.

    Article  Google Scholar 

  20. Bhawsar, A., Pandey, Y., & Singh, U. (2020). Detection and prevention of wormhole attack using the trust-based routing system. In 2020 International conference on electronics and sustainable communication systems (ICESC) (pp. 809–814). IEEE.

  21. Narayanan, S. S., & Murugaboopathi, G. (2020). Modified secure AODV protocol to prevent wormhole attack in MANET. Concurrency and Computation: Practice and Experience, 32(4), e5017.

    Google Scholar 

  22. Tiruvakadu, D. S. K., & Pallapa, V. (2018). Confirmation of wormhole attack in MANETs using honeypot. Computers and Security, 76, 32–49.

    Article  Google Scholar 

  23. Spurthy, K., & Shankar, T. N. (2020). An efficient cluster-based approach to thwart wormhole attack in adhoc networks. International Journal of Advanced Computer Science and Applications, 11(9), 312–316.

    Google Scholar 

  24. Gayathri, S., Seetharaman, R., Subramanian, L. H., Premkumar, S., Viswanathan, S., & Chandru, S. (2019). Wormhole attack detection using energy model in MANETs. In 2019 2nd international conference on power and embedded drive control (ICPEDC) (pp. 264–268). IEEE.

  25. As’adi, H., Keshavarz-Haddad, A., & Jamshidi, A. (2018). A new statistical method for wormhole attack detection in MANETs. In 2018 15th international ISC (Iranian society of cryptology) conference on information security and cryptology (ISCISC) (pp. 1–6), IEEE.

  26. Amish, P., & Vaghela, V. B. (2016). Detection and prevention of wormhole attack in wireless sensor network using AOMDV protocol. Procedia Computer Science 79, 700–707.

  27. Zardari, Z. A., Memon, K. A., Shah, R. A., Dehraj, S., & Ahmed, I. (2021). A lightweight technique for detection and prevention of wormhole attack in MANET. EAI Endorsed Transactions on Scalable Information Systems, 8(29), e2.

    Google Scholar 

  28. Sun, P., Liu, H., Zhang, Y., Tu, L., & Meng, Q. (2021). An intensify atom search optimization for engineering design problems. Applied Mathematical Modelling, 89, 837–859.

    Article  Google Scholar 

  29. Parmar, A., & Vaghela, V. B. (2016). Detection and prevention of wormhole attack in wireless sensor network using AOMDV protocol. Procedia Computer Science 79, 700–707.

  30. Narayanan, S. S., & Murugaboopathi, G. (2020). Modified secure AODV protocol to prevent wormhole attack in MANET. Concurrency and Computation: Practice and Experience, 32(4), 5017.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, S.A. Engineering College (Autonomous), Poonamallee-Avadi Road, Thiruverkadu, Chennai, Tamil Nadu, 600077, India

    D. Hemanand

  2. Department of Information Technology, KGISL Institute of Technology, Saravanampatti, Coimbatore, Tamil Nadu, 641035, India

    N. Sankar Ram

  3. Department of Physics, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, Tamil Nadu, 600119, India

    D. S. Jayalakshmi

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  1. D. Hemanand
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  3. D. S. Jayalakshmi
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Correspondence to D. Hemanand.

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Hemanand, D., Ram, N.S. & Jayalakshmi, D.S. FSSAM: A Five Stage Security Analysis Model for Detecting and Preventing Wormhole Attack in Mobile Ad-Hoc Networks Using Adaptive Atom Search Algorithm. Wireless Pers Commun 128, 487–506 (2023). https://doi.org/10.1007/s11277-022-09964-9

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  • DOI: https://doi.org/10.1007/s11277-022-09964-9

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