skip to main content
10.1145/3484824.3484915acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdsmlaiConference Proceedingsconference-collections
research-article

An Intrusion Prevention System embedded AODV to protect Mobile Adhoc Network against Sybil Attack

Authors Info & Claims
Published:13 January 2022Publication History

ABSTRACT

Mobile Ad hoc Network (MANET) is a group of self-directed, self-organizing and freely moving mobile nodes connected through wireless links, without the support of any central infrastructure. In MANET, every node has a freedom to cooperate in data packet forwarding. MANET is susceptible to various kinds of routing attacks because of its dynamicity, mobility, open medium, and lack of central administration. Thus, security in MANET routing protocol is a vital issue and needs a mechanism to protect communication between nodes. In MANET, a protrusive attack which reduces the network performance is Sybil attack which theft the identities of genuine nodes and impersonate them and drops the packets. In this Paper, Sybil attack detection and prevention (SDP) mechanism is proposed which works as an intrusion detection and prevention system to detect and prevent the MANET against Sybil attack. The proposed SDP mechanism used historical profile analysis and blocking based mechanism that check the real-time as well as previous history of nodes to watch the behavior of Sybil node. Two scenarios of SDP mechanism have been implemented in NS-2 and evaluated with packet delivery ratio, normal routing load, delay and throughput. Accuracy of detection is checked with confusion matrix analysis and found that proposed system gives 90.7% and 97.85% true positive ratio in SDP-I and SDP-II scenarios respectively.

References

  1. Nema, A., Tiwari, B. and Tiwari, V., 2016, March. Improving accuracy for intrusion detection through layered approach using support vector machine with feature reduction. In Proceedings of the ACM Symposium on Women in Research 2016 (pp. 26--31.Google ScholarGoogle Scholar
  2. Saha, P. and Nath, A., 2016. An Overview on Mobile Ad-Hoc Networks. International Journal of Multidisciplinary Research and Modern Education (IJMRME), ISSN, pp.2454--6119.Google ScholarGoogle Scholar
  3. Douceur, J.R., 2002, March. The sybil attack. In International workshop on peer-to-peer systems (pp. 251--260. Springer, Berlin, Heidelberg.Google ScholarGoogle ScholarCross RefCross Ref
  4. Parno, B. and Perrig, A., 2005, November. Challenges in securing vehicular networks. In Workshop on hot topics in networks (HotNets-IV) (pp. 1--6.Google ScholarGoogle Scholar
  5. Hoeper, K. and Gong, G., 2007. Bootstrapping security in mobile ad hoc networks using identity-based schemes. In Security in Distributed and Networking Systems (pp. 313--337.Google ScholarGoogle Scholar
  6. Hashmi, S. and Brooke, J., 2010, July. Towards sybil resistant authentication in mobile ad hoc networks. In 2010 Fourth international conference on emerging security information, systems and technologies (pp. 17--24. IEEE.Google ScholarGoogle Scholar
  7. Chen, Y., Yang, J., Trappe, W. and Martin, R.P., 2010. Detecting and localizing identity-based attacks in wireless and sensor networks. IEEE Transactions on Vehicular Technology, 59(5), pp.2418--2434.Google ScholarGoogle ScholarCross RefCross Ref
  8. Bouassida, M.S., Guette, G., Shawky, M. and Ducourthial, B., 2009. Sybil Nodes Detection Based on Received Signal Strength Variations within VANET. Int. J. Netw. Secur., 9(1), pp.22--33.Google ScholarGoogle Scholar
  9. Pu, C., 2020. Sybil attack in RPL-based internet of things: analysis and defenses. IEEE Internet of Things Journal, 7(6), pp.4937--4949.Google ScholarGoogle ScholarCross RefCross Ref
  10. Rajadurai, H. and Gandhi, U.D., 2020. Fuzzy based collaborative verification system for Sybil attack detection in MANET. Wireless Personal Communications, 110(4), pp.2179--2193.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Akasapu, A. K., Thangavel, G., &Pulugurtha, K. S. R., 2020. An Approach to Identify the Sybil Attacks in Vehicular Ad-Hoc Networks Using Path Signature. International journal of scientific & technology research, volume 9, issue 03, march 2020Google ScholarGoogle Scholar
  12. Sastry, A.S., Chitlapalli, S.S. and Akhila, S., 2019, February. Work-in-Progress: A Novel Approach to Detection and Avoid Sybil Attack in MANET. In International Conference on Remote Engineering and Virtual Instrumentation (pp. 429--441. Springer, Cham.Google ScholarGoogle Scholar
  13. Yuan, Y., Huo, L., Wang, Z. and Hogrefe, D., 2018. Secure APIT localization scheme against sybil attacks in distributed wireless sensor networks. IEEE Access, 6, pp.27629--27636.Google ScholarGoogle ScholarCross RefCross Ref
  14. Mishra, A.K., Tripathy, A.K., Puthal, D. and Yang, L.T., 2018. Analytical model for sybil attack phases in internet of things. IEEE Internet of Things Journal, 6(1), pp.379--387.Google ScholarGoogle ScholarCross RefCross Ref
  15. Dejene, D., Tiwari, B. and Tiwari, V., 2020. TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT. International Journal of Interactive Multimedia & Artificial Intelligence, 6(4).Google ScholarGoogle Scholar
  16. Khalil, M. and Azer, M.A., 2018, April. Sybil attack prevention through identity symmetric scheme in vehicular ad-hoc networks. In 2018 Wireless Days (WD) (pp. 184--186. IEEE.Google ScholarGoogle Scholar
  17. Kumari, R. and Dutta, M., 2018, December. Efficient Approaches to Mitigate the Effect of Sybil Attack in MANET for High Network Lifetime: A review. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 557--562. IEEE.Google ScholarGoogle Scholar
  18. Chauhan, D.S. and Bakshi, S., 2018. Mitigation of Sybil attack in MANET using GA with Fuzzy Logic. Management, 3(4)., pp. 12--19. 2018.Google ScholarGoogle Scholar
  19. Yadav, S., Tiwari, V. and Tiwari, B., 2016, March. Privacy preserving data mining with abridge time using vertical partition decision tree. In Proceedings of the ACM Symposium on Women in Research 2016 (pp. 158--164.Google ScholarGoogle Scholar

Index Terms

  1. An Intrusion Prevention System embedded AODV to protect Mobile Adhoc Network against Sybil Attack
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            DSMLAI '21': Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence
            August 2021
            415 pages
            ISBN:9781450387637
            DOI:10.1145/3484824

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 13 January 2022

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader