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Trust-Aware Fuzzy Evaluation Method for Preventive Route Generation in Mobile Network

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Abstract

The existence of a non-cooperative or black hole node as an intermediate node in a mobile network can degrade the performance of the network and affects the trust of neighbor nodes. In this paper, a trust-aware routing protocol is defined for improving the routing reliability against black hole attacks. A new Trust aware and fuzzy regulated AODV (TFAODV) protocol is investigated in this work as an improvement over the existing AODV protocol. The session-driven evaluation of stability, communication-delay, and failure-ratio parameters are conducted for evaluating the trust of nodes. The fuzzy rules apply to these parameters for computing the degree of trust. This trust vector isolates the attack-suspected and trustful nodes. The proposed TFAODV protocol used the trustful mobile nodes as the intermediate path nodes. The proposed protocol has been experimented with in the NS2 simulation environment. The analytical results are obtained in terms of PDR ratio, Packet Communication, Loss rate parameters. The comparative results are derived against the AODV, Probabilistic AODV, PDS-AODV, PSAODV, and Juneja et al. protocols. The analysis is performed on different scenarios varied in terms of network density, degree of stability, and the number of attackers. The simulation results ensured the proposed TFAODV protocol has improved the PDR ratio and reduced the communication loss significantly against these state-of-art protocols.

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References

  1. Mwangi, E. G., Muketha, G. M., & Ndungu, G. K. (2019). A review of security techniques against black hole attacks in mobile ad hoc networks. In IST-Africa week conference (IST-Africa) (pp. 1–8).

  2. Gurung, S., & Chauhan, S. (2019). Performance analysis of black-hole attack mitigation protocols under gray-hole attacks in MANET. Wireless Networks, 25, 975–988.

    Article  Google Scholar 

  3. Nabou, A., Laanaoui, M. D., & Ouzzif, M. (2018). Evaluation of MANET routing protocols under black hole attack using AODV and OLSR in NS3. In 6th international conference on wireless networks and mobile communications (WINCOM) (pp. 1–6).

  4. Tseng, F. H., Chiang, H. P., & Chao, H. C. (2018). Black hole along with other attacks in MANETs: A survey. Journal of Information Processing Systems, 14(1), 56–78.

    Google Scholar 

  5. Gurung, S., & Chauhan, S. (2019). A survey of black-hole attack mitigation techniques in MANET: Merits, drawbacks, and suitability. Wireless Networks, 26, 1981–2011.

    Article  Google Scholar 

  6. Golchha, P., & Kumar, H. (2018). A survey on black hole attack in MANET using AODV. In International conference on advances in computing, communication control and networking (ICACCCN) (pp. 361–365).

  7. Pooja, V. S., Rohit, T., Reddy, N. M., & Sudeshna, S. (2018). Mobile ad-hoc networks security aspects in black hole attack. In Second international conference on electronics, communication and aerospace technology (ICECA) (pp. 26–30).

  8. Dhende, S. L., Shirbahadurkar, S. D., Musale, S. S., & Galande, S. K. (2018). A survey on black hole attack in mobile ad hoc networks. In 4th international conference on recent advances in information technology (RAIT) (pp. 1–7).

  9. Mistry, M., Tandel, P., & Reshamwala, V. (2017). Mitigating techniques of black hole attack in MANET: A review. In International conference on trends in electronics and Informatics (ICEI) (pp. 554–557).

  10. Satav, P. R., Jawandhiya, P. M., & Thakare, V. M. (2018). Secure route selection mechanism in the presence of black hole attack with AOMDV routing algorithm. In Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1–6).

  11. Gurung, S., & Chauhan, S. (2018). A dynamic threshold based approach for mitigating black-hole attack in MANET. Wireless Networks, 24(8), 2957–2971.

    Article  Google Scholar 

  12. Mohammad, S. N., Singh, R. P., Dey, A., & Ahmad, S. J. (2018). ESMBCRT: Enhance security to MANETs against black hole attack using MCR technique. In Innovations in electronics and communication engineering (pp. 319–326).

  13. Hammamouche, A., Omar, M., Djebari, N., & Tari, A. (2018). Lightweight reputation-based approach against simple and cooperative black-hole attacks for MANET. Journal of Information Security and Applications, 43, 12–20.

    Article  Google Scholar 

  14. Rajendran, N., Jawahar, P., & Priyadarshini, R. (2019). Cross centric intrusion detection system for secure routing over black hole attacks in MANETs. Computer Communications, 148, 129–135.

    Article  Google Scholar 

  15. Mahin, S. H., Taranum, F., Fatima, L. N., & Khan, K. U. (2019). Detection and interception of black hole attack with justification using anomaly based intrusion detection system in MANETs. International Journal of Recent Technology and Engineering, 8(11), 2392–2398.

    Google Scholar 

  16. Albalas, F., Yaseen, M. B., & Nassar, A. (2019). Detecting black hole attacks in MANET using relieff classification algorithm. In 5th international conference on engineering and MIS (ICEMIS'19) (p. 6).

  17. Tiruvakadu, D. S., & Pallapa, V. (2018). Honeypot based black-hole attack confirmation in a MANET. International Journal of Wireless Information Networks, 25, 434–448.

    Article  Google Scholar 

  18. Sivanesh, S., & Dhulipala, V. (2020). Accurate and cognitive intrusion detection system (ACIDS): A novel black hole detection mechanism in mobile ad hoc networks. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01505-2.

    Article  Google Scholar 

  19. Sundar, S., & Kittur, H. M. (2019). Random ID based secure AODV to prevent black hole attack in MANET. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12), 1725–1729.

    Article  Google Scholar 

  20. Sharma, D. (2019). Efficient detection of black hole attack in mobile adhoc networks using a TRUST based scheme. International Journal of Recent Technology and Engineering, 8(4), 2740–2744.

    Google Scholar 

  21. Sharma, D. (2019). DRI-based implementation for detecting and eliminating cooperative black hole nodes in MANET. International Journal of Recent Technology and Engineering, 8(3), 8192–8198.

    Google Scholar 

  22. Saranya, R., & Rajesh, R. (2019). Utilization of energy consumption metric to detect black hole attacker in dsr routing protocol. International Journal of Recent Technology and Engineering, 8, 6116–6120.

    Google Scholar 

  23. Tamilselvi, P., & Babu, C. G. (2019). An efficient approach to circumvent black hole nodes in manets. Cluster Computing, 22(5), 11401–11409.

    Article  Google Scholar 

  24. Aravindhar, D. J., Sophia, S. G., Krishnan, P., & Kumar, D. P. (2019). Minimization of black hole attacks in adhoc networks using risk aware response mechanism. In 3rd international conference on electronics, communication and aerospace technology (ICECA) (pp. 1391–1394).

  25. Khamayseh, Y., Yassein, M. B., & Abu-Jazoh, M. (2019). Intelligent black hole detection in mobile AdHoc networks. International Journal of Electrical and Computer Engineering, 9(3), 1968–1977.

    Google Scholar 

  26. Delkesh, T., & Jamali, M. A. (2019). EAODV: Detection and removal of multiple black hole attacks through sending forged packets in MANETs. Journal of Ambient Intelligence and Humanized Computing, 10, 1897–1914.

    Article  Google Scholar 

  27. Gurung, S., & Chauhan, S. (2019). A dynamic threshold based algorithm for improving security and performance of AODV under black-hole attack in MANET. Wireless Networks, 25, 1685–1695.

    Article  Google Scholar 

  28. Arulkumaran, G., & Gnanamurthy, R. K. (2019). Fuzzy trust approach for detecting black hole attack in mobile adhoc network. Mobile Networks and Applications, 24(2), 386–393.

    Article  Google Scholar 

  29. Merlin, R. T., & Ravi, R. (2019). Novel trust based energy aware routing mechanism for mitigation of black hole attacks in MANET. Wireless Personal Communications, 104, 1599–1636.

    Article  Google Scholar 

  30. Dorri, A., Vaseghi, S., & Gharib, O. (2018). DEBH: Detecting and eliminating black holes in mobile ad hoc network. Wireless Networks, 24, 2943–2955.

    Article  Google Scholar 

  31. Yasin, A., & Zant, M. A. (2018). Detecting and isolating black-hole attacks in MANET using timer based baited technique. Wireless Communications and Mobile Computing, 2018, 10.

    Article  Google Scholar 

  32. Elmahdi, E., Yoo, S. M., & Sharshembiev, K. (2020). Secure and reliable data forwarding using homomorphic encryption against blackhole attacks in mobile ad hoc networks. Journal of Information Security and Applications, 51, 102425.

    Article  Google Scholar 

  33. Liu, K., & Zhou, L. (2020). Routing authentication chain mechanism to resist AODV black hole attacks. In ACM international conference proceeding series (pp. 53–57).

  34. Priya, S., & Suganthy, P. (2019). A trust based multipath routing for black hole attacks with group search optimization routing. International Journal of Recent Technology and Engineering, 8(3), 3407–3415.

    Google Scholar 

  35. Keerthika, V., & Malarvizhi, N. (2019). Mitigate black hole attack using hybrid bee optimized weighted trust with 2-Opt AODV in MANET. Wireless Personal Communications, 106, 621–632.

    Article  Google Scholar 

  36. Thanuja, R., & Umamakeswari, A. (2019). Black hole detection using evolutionary algorithm for IDS/IPS in MANETs. Cluster Computing, 22(2), 3131–3143.

    Article  Google Scholar 

  37. Moudni, H., Er-rouidi, M., Mouncif, H., & Hadadi, B. E. (2019). Black hole attack detection using fuzzy based intrusion detection systems in MANET. Procedia Computer Science, 151, 1176–1181.

    Article  Google Scholar 

  38. Fahad, A. M., Ahmed, A. A., Alghushami, A. H., & Alani, S. (2018). Detection of black hole attacks in mobile ad hoc networks via HSA-CBDS method. In International conference on intelligent computing & optimization (pp. 46–55).

  39. Juneja, K. (2020). DRI table based traffic-behaviour analysis approach for detection of blackhole attack. International Journal of Sensors, Wireless Communications and Control, 10(1), 79–93.

    Article  Google Scholar 

  40. Juneja, K. (2019). Probabilistic dempster shafer based communication behaviour analysis for attack safe communication in mobile network. Pertanika Journal of Science and Technology, 27(3), 1301–1316.

    Google Scholar 

  41. Juneja, K. (2020). Random-session and K-neighbour based suspected node analysis approach for cooperative blackhole detection in MANET. Wireless Personal Communications, 110(1), 45–68.

    Article  Google Scholar 

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Correspondence to Kapil Juneja.

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Juneja, K. Trust-Aware Fuzzy Evaluation Method for Preventive Route Generation in Mobile Network. Wireless Pers Commun 119, 3673–3697 (2021). https://doi.org/10.1007/s11277-021-08426-y

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