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ipBF: A Fast and Accurate IP Address Lookup Using 3D Bloom Filter

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

IP address lookup is a crucial part of router in Computer Network. There are millions of IP addresses to be searched per second. Hence, it is immensely necessitated to enhance the performance of the IP address lookup. Therefore, this paper presents a novel approach of IP address lookup using 3D Bloom Filter, called ipBF. ipBF inherits the properties of 3D Bloom Filter. Thus, ipBF features - (a) high accuracy, (b) low memory consumption, and (c) high performance. In addition, ipBF consumes \(8-bits\) per IP address which is very less as compared to its contemporary solution. Besides, ipBF filters the false positive probability in eight layers by deploying eight 3D Bloom Filters. Hence, ipBF is able achieve higher accuracy. We show the accuracy using theoretical calculations.

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References

  1. Almeida, P.S., Baquero, C., Preguica, N., Hutchison, D.: Scalable bloom filters. Inf. Process. Lett. 101(6), 255–261 (2007)

    Article  MathSciNet  Google Scholar 

  2. Antikainen, M., Aura, T., Särelä, M.: Denial-of-service attacks in bloom-filter-based forwarding. IEEE/ACM Trans. Netw. 22(5), 1463–1476 (2014). https://doi.org/10.1109/TNET.2013.2281614

    Article  Google Scholar 

  3. Appleby, A.: Murmur hash (2018). https://sites.google.com/site/murmurhash/. Accessed Aug 2018

  4. Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). https://doi.org/10.1145/362686.362692

    Article  MATH  Google Scholar 

  5. Bringer, J., Morel, C., Rathgeb, C.: Security analysis and improvement of some biometric protected templates based on bloom filters. Image Vis. Comput. 58(Supplement C), 239–253 (2017). https://doi.org/10.1016/j.imavis.2016.08.002

    Article  Google Scholar 

  6. Byun, H., Lim, H.: IP address lookup algorithm using a vectored bloom filter. Trans. Korean Inst. Electr. Eng. 65(12), 2061–2068 (2016). https://doi.org/10.5370/kiee.2016.65.12.2061

    Article  Google Scholar 

  7. Calderoni, L., Palmieri, P., Maio, D.: Location privacy without mutual trust: the spatial bloom filter. Comput. Commun. 68(Supplement C), 4–16 (2015). https://doi.org/10.1016/j.comcom.2015.06.011. Security and Privacy in Unified Communications: Challenges and Solutions

    Article  MATH  Google Scholar 

  8. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2), 4:1–4:26 (2008). https://doi.org/10.1145/1365815.1365816

    Article  Google Scholar 

  9. Fan, B., Andersen, D.G., Kaminsky, M., Mitzenmacher, M.D.: Cuckoo filter: practically better than bloom. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, pp. 75–88. ACM, New York (2014). https://doi.org/10.1145/2674005.2674994

  10. Gao, W., Nguyen, J., Wu, Y., Hatcher, W.G., Yu, W.: A bloom filter-based dual-layer routing scheme in large-scale mobile networks. In: 2017 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9 (2017). https://doi.org/10.1109/ICCCN.2017.8038405

  11. Geravand, S., Ahmadi, M.: Bloom filter applications in network security: a state-of-the-art survey. Comput. Netw. 57(18), 4047–4064 (2013). https://doi.org/10.1016/j.comnet.2013.09.003

    Article  Google Scholar 

  12. Gomez-Barrero, M., Rathgeb, C., Li, G., Ramachandra, R., Galbally, J., Busch, C.: Multi-biometric template protection based on bloom filters. Inf. Fusion 42(Supplement C), 37–50 (2018). https://doi.org/10.1016/j.inffus.2017.10.003

    Article  Google Scholar 

  13. Grandi, F.: On the analysis of bloom filters. Inf. Process. Lett. 129, 35–39 (2018). https://doi.org/10.1016/j.ipl.2017.09.004

    Article  MathSciNet  MATH  Google Scholar 

  14. Hua, Y., Zhu, Y., Jiang, H., Feng, D., Tian, L.: Supporting scalable and adaptive metadata management in ultralarge-scale file systems. IEEE Trans. Parallel Distrib. Syst. 22(4), 580–593 (2011)

    Article  Google Scholar 

  15. Huo, Z., Xiao, L., Zhong, Q., Li, S., Li, A., Rua, L., Wang, S., Fu, L.: MBFS: a parallel metadata search method based on bloomfilters using mapreduce for large-scale file systems. J. Supercomput. 72(8), 1–27 (2015)

    Google Scholar 

  16. Jackman, S.D., Vandervalk, B.P., Mohamadi, H., Chu, J., Yeo, S., Hammond, S.A., Jahesh, G., Khan, H., Coombe, L., Warren, R.L., et al.: ABySS 2.0: resource-efficient assembly of large genomes using a bloom filter. Genome Res. 27(5), 768–777 (2017)

    Article  Google Scholar 

  17. Kwon, M., Reviriego, P., Pontarelli, S.: A length-aware cuckoo filter for faster IP lookup. In: 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1071–1072 (2016). https://doi.org/10.1109/INFCOMW.2016.7562258

  18. Lin, F., Wang, G., Zhou, J., Zhang, S., Yao, X.: High-performance IPv6 address lookup in GPU-accelerated software routers. J. Netw. Comput. Appl. 74, 1–10 (2016). https://doi.org/10.1016/j.jnca.2016.08.004

    Article  Google Scholar 

  19. Lucchesi, A., Drummond, A.C., Teodoro, G.: High-performance IP lookup using Intel Xeon Phi: a bloom filters based approach. J. Internet Serv. Appl. 9(1), 3 (2018). https://doi.org/10.1186/s13174-017-0075-y

    Article  Google Scholar 

  20. Maccari, L., Fantacci, R., Neira, P., Gasca, R.M.: Mesh network firewalling with bloom filters. In: 2007 IEEE International Conference on Communications, ICC 2007, pp. 1546–1551. IEEE (2007)

    Google Scholar 

  21. Marandi, A., Braun, T., Salamatian, K., Thomos, N.: BFR: a bloom filter-based routing approach for information-centric networks. arXiv preprint arXiv:1702.00340 (2017)

  22. Melsted, P., Pritchard, J.K.: Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinform. 12(1), 333 (2011). https://doi.org/10.1186/1471-2105-12-333

    Article  Google Scholar 

  23. Mun, J.H., Lim, H.: New approach for efficient IP address lookup using a bloom filter in trie-based algorithms. IEEE Trans. Comput. 65(5), 1558–1565 (2016). https://doi.org/10.1109/TC.2015.2444850

    Article  MathSciNet  MATH  Google Scholar 

  24. Patgiri, R., Nayak, S., Borgohain, S.K.: rDBF: a r-dimensional bloom filter for massive scale membership query. J. Netw. Comput. Appl. (2019). Accepted

    Google Scholar 

  25. Sadhya, D., Singh, S.K.: Providing robust security measures to bloom filter based biometric template protection schemes. Comput. Secur. 67(Supplement C), 59–72 (2017). https://doi.org/10.1016/j.cose.2017.02.013

    Article  Google Scholar 

  26. Sarela, M., Rothenberg, C.E., Aura, T., Zahemszky, A., Nikander, P., Ott, J.: Forwarding anomalies in bloom filter-based multicast. In: 2011 Proceedings IEEE INFOCOM, pp. 2399–2407 (2011). https://doi.org/10.1109/INFCOM.2011.5935060

  27. Sasaki, K., Nakao, A.: Packet cache network function for peer-to-peer traffic management with bloom-filter based flow classification. In: 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–6 (2016). https://doi.org/10.1109/APNOMS.2016.7737214

  28. Singh, A., Garg, S., Batra, S., Kumar, N., Rodrigues, J.J.: Bloom filter based optimization scheme for massive data handling in IoT environment. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.12.016

    Article  Google Scholar 

  29. Xiao, P., Li, Z., Qi, H., Qu, W., Yu, H.: An efficient DDoS detection with bloom filter in SDN. In: 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 1–6 (2016). https://doi.org/10.1109/TrustCom.2016.0038

  30. Zhu, Y., Jiang, H., Wang, J., Xian, F.: HBA: distributed metadata management for large cluster-based storage systems. IEEE Trans. Parallel Distrib. Syst. 19(6), 750–763 (2008)

    Article  Google Scholar 

  31. Ziegeldorf, J.H., Pennekamp, J., Hellmanns, D., Schwinger, F., Kunze, I., Henze, M., Hiller, J., Matzutt, R., Wehrle, K.: BLOOM: BLoom filter based oblivious outsourced matchings. BMC Med. Genomics 10(2), 44 (2017). https://doi.org/10.1186/s12920-017-0277-y

    Article  Google Scholar 

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Acknowledgement

Authors would like to acknowledge TEQIP-III, NIT Silchar for supporting this research work.

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Authors and Affiliations

  1. National Institute of Technology Silchar, Silchar, 788010, Assam, India

    Ripon Patgiri, Samir Kumar Borgohain & Sabuzima Nayak

Authors
  1. Ripon Patgiri
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  2. Samir Kumar Borgohain
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  3. Sabuzima Nayak
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Corresponding author

Correspondence to Ripon Patgiri .

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Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Patgiri, R., Borgohain, S.K., Nayak, S. (2020). ipBF: A Fast and Accurate IP Address Lookup Using 3D Bloom Filter. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_18

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