access icon free RBFNN: a radial basis function neural network model for detecting and mitigating the cache pollution attacks in named data networking

Named data networking (NDN) corresponds to content-centric networking, content-based networking, and data-oriented networking. Future internet architecture is modelled to overcome the fundamental limitations of the present internet protocol-based internet and to provide specific strong security. Caching is a key NDN feature in the network. However, pervasive caching strengthens security issues in particular cache pollution assaults including cache poisoning (e.g. presenting malicious content in caches as false-locality) and cache pollution (e.g. unpopular content is ruined with cache locality as locality-disruption). In this work, a new cache replacement method based on the radial basis function neural network (RBFNN) is proposed to detect and mitigate the cache pollution attacks in NDN. RBFNN framework is constructed utilising the input associated with the cached content inherent characteristics and output data associated with the content type, i.e. locality-disruption, false-locality, and healthy. Experimental results show the efficiency as well as the effectiveness of the proposed method in terms of hit damage ratio and computational time.

Inspec keywords: protocols; radial basis function networks; cache storage; Internet; neural nets; computer network security

Other keywords: cache pollution attack detection; radial basis function neural network model; pervasive caching; cache pollution attack mitigation; NDN; content-based networking; Internet protocol-based Internet; Internet architecture; content-centric networking; data-oriented networking; named data networking; RBFNN

Subjects: Neural computing techniques; Protocols; Other computer networks; Computer communications; Data security; Protocols

References

    1. 1)
      • 25. Mirzaeinejad, H.: ‘Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network’, Appl. Soft Comput., 2018, 70, pp. 318329.
    2. 2)
      • 8. Conti, M., Gasti, P., Teoli, M.: ‘A lightweight mechanism for detection of cache pollution attacks in named data networking’, Comput. Netw., 2013, 57, (16), pp. 31783191.
    3. 3)
      • 18. Li, H., Zhou, H., Quan, W., et al: ‘CCNHCaching: a high-speed caching throughput simulator for information-centric networks’, J. Internet Technol., 2019, 20, (3), pp. 705715.
    4. 4)
      • 27. Park, H., Widjaja, I., Lee, H.: ‘Detection of cache pollution attacks using randomness checks’. 2012 IEEE Int. Conf. on Communications (ICC), Ottawa, ON, Canada, June 2012, pp. 10961100.
    5. 5)
      • 23. Ali Ahmed, W., Shamsuddin, S.: ‘Neuro-fuzzy system in partitioned client-side web cache’, Expert Syst. Appl., 2011, 38, (12), pp. 1471514725.
    6. 6)
      • 26. Afanasyev, A., Moiseenko, I., Zhang, L.: ‘ndnSIM: NDN simulator for NS-3’. Tech. Rep, University of California, Los Angeles, 2012, vol. 4.
    7. 7)
      • 20. Quan, W., Xu, C., Vasilakos, A.V.., et al: ‘TB2F: tree-bitmap and bloom-filter for a scalable and efficient name lookup in content-centric networking’. 2014 IFIP Networking Conf., Trondheim, Norway, 2014, pp. 19.
    8. 8)
      • 17. Liu, G., Quan, W., Cheng, N., et al: ‘Efficient DDoS attacks mitigation for stateful forwarding in internet of things’, J. Netw. Comput. Appl., 2019, 130, pp. 13.
    9. 9)
      • 22. Kaya, C.C., Zhang, G., Tan, Y., et al: ‘An admission-control technique for delay reduction in proxy caching’, Decis. Support Syst., 2009, 46, (2), pp. 594603.
    10. 10)
      • 2. Salah, H., Strufe, T.: ‘Evaluating and mitigating a collusive version of the interest flooding attack in NDN’. 2016 IEEE Symp. on Computers and Communication (ISCC), Messina, Italy, June 2016, pp. 938945.
    11. 11)
      • 13. Guo, H., Wang, X., Chang, K., et al: ‘Exploiting path diversity for thwarting pollution attacks in named data networking’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (9), pp. 20772090.
    12. 12)
      • 15. Lim, H., Ni, A., Kim, D., et al: ‘NDN construction for big science: lessons learned from establishing a Testbed’, IEEE Netw., 2018, 32, (6), pp. 124136.
    13. 13)
      • 19. Quan, W., Xu, C., Guan, J., et al: ‘Scalable name lookup with adaptive prefix bloom filter for named data networking’, IEEE Commun. Lett., 2013, 18, (1), pp. 102105.
    14. 14)
      • 1. Zhi, T., Luo, H., Liu, Y.: ‘A Gini impurity-based interest flooding attack defence mechanism in NDN’, IEEE Commun. Lett., 2018, 22, (3), pp. 538541.
    15. 15)
      • 9. Ghali, C., Tsudik, G., Uzun, E.: ‘Needle in a haystack: mitigating content poisoning in named-data networking’. Proc. NDSS Workshop on Security of Emerging Networking Technologies (SENT), San Diego, California, February 2014.
    16. 16)
      • 28. Karami, A., Guerrero-Zapata, M.: ‘An ANFIS-based cache replacement method for mitigating cache pollution attacks in named data networking’, Compu. Netw., 2015, 80, pp. 5165.
    17. 17)
    18. 18)
      • 4. Fan, C.I., Chen, I.T., Cheng, C.K., et al: ‘FTP-NDN: file transfer protocol based on re-encryption for named data network supporting nondesignated receivers’, IEEE Syst. J., 2016, 12, (1), pp. 473484.
    19. 19)
      • 12. Rezaeifar, Z., Wang, J., Oh, H.: ‘A trust-based method for mitigating cache poisoning in name data networking’, J. Netw. Comput. Appl., 2018, 104, pp. 117132.
    20. 20)
      • 10. Li, Q., Lee, P.P., Zhang, P., et al: ‘Capability-based security enforcement in named data networking’, IEEE/ACM Trans. Netw., 2017, 25, (5), pp. 27192730.
    21. 21)
      • 3. Hu, X., Gong, J., Cheng, G., et al: ‘Mitigating content poisoning with name-key based forwarding and multipath forwarding based Inband probe for energy management in smart cities’, IEEE Access, 2018, 6, pp. 3969239704.
    22. 22)
      • 11. Mai, H.L., Nguyen, T., Doyen, G., et al: ‘Towards a security monitoring plane for named data networking and its application against content poisoning attack’. NOMS 2018 – 2018 IEEE/IFIP Network Operations and Management Symp., Taipei, Taiwan, April 2018, pp. 19.
    23. 23)
      • 21. Deng, L., Gao, Y., Chen, Y., et al: ‘Pollution attacks and defenses for internet caching systems’, Comput. Netw., 2008, 52, (5), pp. 935956.
    24. 24)
      • 16. Wang, L., Zhang, Z., Dong, M., et al: ‘Securing named data networking: attribute-based encryption and beyond’, IEEE Commun. Mag., 2018, 56, (11), pp. 7681.
    25. 25)
      • 7. Kondo, D., Silverston, T., Vassiliades, V., et al: ‘Name filter: a countermeasure against information leakage attacks in named data networking’, IEEE Access, 2018, 6, pp. 6515165170.
    26. 26)
      • 5. Saxena, D., Raychoudhury, V., Suri, N., et al: ‘Named data networking: a survey’, Comput. Sci. Rev., 2016, 19, pp. 1555.
    27. 27)
      • 6. Yu, Y., Li, Y., Du, X., et al: ‘Content protection in named data networking: challenges and potential solutions’, IEEE Commun. Mag., 2018, 56, (11), pp. 8287.
    28. 28)
      • 14. Kim, D., Bi, J., Vasilakos, A.V., et al: ‘Security of cached content in NDN’, IEEE Trans. Inf. Forensics Sec., 2017, 12, (12), pp. 29332944.
    29. 29)
      • 29. Chen, H., Xiao, Y., Vrbsky, S.V.: ‘An update-based step-wise optimal cache replacement for wireless data access’, Comput. Netw., 2013, 57, (1), pp. 197212.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-net.2019.0156
Loading

Related content

content/journals/10.1049/iet-net.2019.0156
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading