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Mitigation of black hole attacks using firefly and artificial neural network

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Abstract

In Mobile Ad hoc Network (MANET), network topology changes as devices/users/nodes move and nodes can serve as a source, destination, or router for the information. One of the challenging tasks in MANET is secure routing because of the change of network topology, open nature of wireless communications and frequent breakage in communication links. Thus, it is critically important to develop a robust and secure routing protocol for sending data from the source to the destination in MANET. In this paper, we investigate black hole attacks where compromised nodes utilize routing protocols for self-exposing which has the shortest route for data forwarding to the destination node. Specifically, the proposed approach enhances the Ad hoc On-Demand Distance Vector routing protocol for combating black hole attacks by leveraging the Firefly Algorithm with Artificial Neural Network. Performance evaluation is carried out using numerical results obtained from several experiments and considering different metrics such as computation overhead, packet delivery rate, throughput and delay. Numerical results show that the proposed approach outperforms the traditional approaches.

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References

  1. Tamilselvan L, Sankaranarayanan V (2008) Prevention of co-operative black hole attack in MANET. J Netw. https://doi.org/10.4304/jnw.3.5.13-20

    Article  Google Scholar 

  2. Mistry N, Jinwala DC, Zaveri M (2010) Improving AODV protocol against blackhole attacks. In: Proceedings of the international multi conference of engineers and computer scientists, vol. 2, pp 17–19

  3. Patcha, Mishra A (2003) Collaborative security architecture for black hole attack prevention in mobile ad hoc networks. In: Proceedings of the radio and wireless conference, pp 75–78

  4. Ramaswamy S, Fu H, Sreekantaradhya M, Dixon J, Nygard KE (2003) Prevention of cooperative black hole attack in wireless ad hoc networks. Int Conf Wirel Netw 2(3):570–575

    Google Scholar 

  5. Modi N, Gupta VK (2014) Prevention of black hole attack using AODV routing protocol in MANET. Int J Comput Sci Inf Technol 5(3):3254–3258

    Google Scholar 

  6. Sharma VC, Gupta A, Dimri V (2013) Detection of black hole attack in MANET under AODV routing protocol. Int J Adv Res Comput Sci Softw Eng 3(6):438–443

    Google Scholar 

  7. Sultana J, Ahmed T (2018) Elliptic Curve Cryptography based data transmission against blackhole attack in MANET. Int J Electr Comput Eng (IJECE) 8(6):4412

    Article  Google Scholar 

  8. Gupta P, Goel P, Varshney P, Tyagi N (2019) Reliability factor based AODV protocol: prevention of black hole attack in MANET. In: Tiwari Shailesh, Trivedi Munesh C, Mishra Krishn K, Misra AK, Kumar Khedo Kavi (eds) Smart innovations in communication and computational sciences. Springer, Singapore, pp 271–279. https://doi.org/10.1007/978-981-13-2414-7_26

    Chapter  Google Scholar 

  9. Dave D, Dave P (2014) An effective Black hole attack detection mechanism using Permutation Based Acknowledgement in MANET. In: Proceedings of the 2014 international conference on advances in computing, communications and informatics (ICACCI), pp 1690–1696

  10. Rani P, Kavita SVerma, Nguyen GN (2020) Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network. IEEE Access 8:121755–121764

    Article  Google Scholar 

  11. Venkanna U, Velusamy RL (2011) Black hole attack and their counter measure based on trust management in MANET: A survey. In: Proceedings of the 3rd international conference on advances in recent technologies in communication and computing

  12. Mohammed A, Sofiane BH, Mohamed FK (2015) A cross layer for detection and ignoring black hole attack in MANET. Int J Comput Netw Inf Secur 7(10):42–49. https://doi.org/10.5815/ijcnis.2015.10.05

    Article  Google Scholar 

  13. Nath I, Chaki R (2012) BHAPSC: a new black hole attack prevention system in clustered MANET. Int J Adv Res Comput Sci Softw Eng 2(8):113–121

    Google Scholar 

  14. Batra I, Verma S, Kavita MA (2020) A lightweight IoT-based security framework for inventory automation using wireless sensor network. Int J Commun Syst 33(4):e4228. https://doi.org/10.1002/dac.4228

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Shona D, Kumar MS (2018) Efficient IDs for MANET using hybrid firefly with a genetic algorithm. In: Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp 191–194

  17. Abdel-Azim M, Salah HED, Eissa ME (2018) IDS Against Black-Hole Attack for MANET. IJ Netw Secur 20(3):585–592

    Google Scholar 

  18. Jain A, Prajapati U, Chouhan P (2016) Trust based mechanism with AODV protocol for prevention of black-hole attack in MANET scenario. In: Proceedings of the 2016 symposium on colossal data analysis and networking (CDAN), pp 1–4

  19. Vijayalakshmi B, Kadarkarayandi Ramar NZ, Jhanjhi SV, Kaliappan M, Vijayalakshmi K, Vimal S, Kavita UG (2021) An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. Int J Commun Syst. https://doi.org/10.1002/dac.4609

    Article  Google Scholar 

  20. Baadache A, Belmehdi A (2012) Fighting against packet dropping misbehavior in multi-hop wireless ad hoc networks. J Netw Comput Appl 35(3):1130–1139

    Article  Google Scholar 

  21. Kolade AT, Zuhairi MF, Yafi E, Zheng CL (2017) Performance analysis of black hole attack in MANET. In: Proceedings of the 11th international conference on ubiquitous information management and communication, pp 1–7

  22. Kumari SV, Paramasivan B (2015) Ant based defense mechanism for selective forwarding attack in MANET. In: Proceedings of the 2015 31st IEEE international conference on data engineering workshops, pp 92–97

  23. Shahabi S, Ghazvini M, Bakhtiarian M (2016) A modified algorithm to improve security and performance of AODV protocol against black hole attack. Wirel Netw 22(5):1505–1511

    Article  Google Scholar 

  24. Gurung S, Chauhan S (2018) A novel approach for mitigating gray hole attack in MANET. Wirel Netw 24(2):565–579

    Article  Google Scholar 

  25. Li W, Chai Y, Khan F et al (2021) A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mob Netw Appl 26:234–252. https://doi.org/10.1007/s11036-020-01700-6

    Article  Google Scholar 

  26. Keerthika V, Malarvizhi N (2019) Mitigate black hole attack using hybrid bee optimized weighted trust with 2-opt AODV in MANET. Wirel Pers Commun 106(2):621–632

    Article  Google Scholar 

  27. Merlin RT, Ravi R (2019) Novel trust-based energy aware routing mechanism for mitigation of black hole attacks in MANET. Wirel Pers Commun 104(4):1599–1636

    Article  Google Scholar 

  28. Medadian M, Yektaie MH, Rahmani AM (2009) Combat with Black hole attack in AODV routing protocol in MANET. In: Proceedings of the 2009 first asian himalayas international conference on internet, pp 530–535

  29. Mohanapriya M, Krishnamurthi I (2014) Modified DSR protocol for detection and removal of selective black hole attack in MANET. Elsevier Comput Electr Eng 40(2):530–538

    Article  Google Scholar 

  30. Choudhary N, Tharani L (2015) Preventing Black Hole Attack in AODV using timer-based detection mechanism. In: Proceedings of the international conference on signal processing and communication engineering systems, pp 1–4

  31. Ahmad SJ, Reddy VSK, Damodaram A, Radha Krishna P (2015) Detection of black hole attack using code division security method. In: Satapathy SC, Govardhan A, Srujan Raju K, Mandal JK (eds) Advances in intelligent systems and computing. Springer, Cham, pp 307–314. https://doi.org/10.1007/978-3-319-13731-5_34

    Chapter  Google Scholar 

  32. Batra I, Verma S, Malik A, Kavita UG, Rodrigues JJPC, Nguyen GN, Sanwar Hosen ASM, Mariappan V (2020) Hybrid logical security framework for privacy preservation in the green internet of things. Sustainability 12(14):5542. https://doi.org/10.3390/su12145542

    Article  Google Scholar 

  33. Zardari ZA, He J, Zhu N, Mohammadani K, Pathan M, Hussain M, Memon M (2019) A dual attack detection technique to identify black and gray hole attacks using an intrusion detection system and a connected dominating set in MANETs. Future Internet 11(3):61. https://doi.org/10.3390/fi11030061

    Article  Google Scholar 

  34. Himral L, Vig V, Chand N (2011) Preventing aodv routing protocol from black hole attack. Int J Eng Sci Technol (IJEST) 3(5):3927–3932

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Engineering, Rayat Bahra University, Mohali, India

    Pooja Rani

  2. Department of Computer Science Engineering, Chandigarh University, Mohali, India

    Kavita & Sahil Verma

  3. Danda B. Rawat is with the Department of Electrical Engineering & Computer Science (EECS), Howard University, Washington, DC, 20059, USA

    Danda B. Rawat

  4. Department of Electronics and Communication Engineering, Raghu Institute of Technology (A), Visakhapatnam, Andhra Pradesh, 531162, India

    Sonali Dash

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  1. Pooja Rani
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  2. Kavita
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  4. Danda B. Rawat
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Correspondence to Danda B. Rawat.

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Rani, P., Kavita, Verma, S. et al. Mitigation of black hole attacks using firefly and artificial neural network. Neural Comput & Applic 34, 15101–15111 (2022). https://doi.org/10.1007/s00521-022-06946-7

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  • DOI: https://doi.org/10.1007/s00521-022-06946-7

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