Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM

Sonali Patil; L. Arun Raj

doi:10.7494/csci.2021.22.3.3968

Authors

Sonali Patil B.S.Abdur Crescent Institute of Science & Technology, Tamil Nadu, India
L. Arun Raj Department of Computer Sc. & Engg, B.S.Abdur Crescent Institute of Science & Technology, Tamil Nadu, India

DOI:

https://doi.org/10.7494/csci.2021.22.3.3968

Keywords:

IoT, network traffic, machine learning, deep learning, classification, , cloud computing

Abstract

Internet of Things (IoT) and cloud based collaborative platforms are emerging as new infrastructures during recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT-cloud based collaborative platforms to utilize the channel capacity optimally for transmitting the benign traffic and to block the malicious traffic. The traffic classification mechanism should be dynamic and capable enough to classify the network traffic in a quick manner, so that the malevolent traffic can be identified in earlier stages and benign traffic can be channelized to the destined nodes speedily. In this paper, we are presenting deep learning recurrent LSTM based technique to classify the traffic over IoT-cloud platforms. Machine learning techniques (MLTs) have also been employed for comparison of the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is classified into three classes namely Tor-Normal, NonTor-Normal and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet classify the traffic accurately and also helps in reducing the network latency and in enhancing the data transmission rate as well as network throughput.

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Author Biography

Sonali Patil, B.S.Abdur Crescent Institute of Science & Technology, Tamil Nadu, India

Department of Computer Science Engineering

References

A. Dainotti A.P., Sansone C.: Early classification of network traffic through multi- classification. In: Traffic Monitoring and Analysis, Lecture Notes in Computer Science, vol. 6613, pp. 122–135, 2011.

Abdelmoniem A., Bensaou B., Abu A.A.: Mitigating incast-TCP congestion in data centers with SDN. In: Mitigating incast-TCP congestion in data centers with SDN, Annals of Telecommunications, vol. 73(3), pp. 263–277, 2018.

Abdelmoniem A.M., Bensaou B., Abu A.J.: SICC: SDN-based incast congestion control for data centers. In: SICC: SDN-based incast congestion control for data centers, 2017 IEEE International Conference on Communications (ICC), pp. 1-6. 2017.

Akram H., Aniruddha G., Pascal B., C. S.D., Thierry G.: Software-defined net- working: challenges and research opportunities for future internet. In: Comput Networks, vol. 75, p. 453-471, 2014.

et al. A.S.: Characterizing and classifying IoT traffic in smart cities and campuses. In: Characterizing and classifying IoT traffic in smart cities and campuses, 2017 IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS), pp. 559–564. 2017.

Auld T., Moore A.W., Gull S.F.: Bayesian Neural Networks for Internet Traffic Classification. In: Bayesian Neural Networks for Internet Traffic Classification, IEEE Transactions on Neural Networks, vol. 18(1), pp. 223–239, 2007.

Bermolen P., Mellia M., Meo M., Rossi D., Valenti S.: Abacus: Accurate be- havioral classification of P2P-TV traffic. In: Computer Networks, vol. 55(6), pp. 1394 – 1411, 2011.

Botta A., de Donato W., Persico V., Pescap A.: Integration of Cloud computing and Internet of Things: A survey. In: Future Generation Computer Systems, vol. 56, pp. 684 – 700, 2016. ISSN 0167-739X. URLhttp://dx.doi.org/https://doi.org/10.1016/j.future.2015.09.021.

Breiman L.: Bagging Predictors. In: Machine Learning, vol. 24(2), pp. 123–140, 1996.

Chamasemani F.F., Singh Y.P.: Multi-class Support Vector Machine (SVM) Clas- sifiers – An Application in Hypothyroid Detection and Classification. In: 2011

Cortes C., Vapnik V.: Support Vector Networks. In: Machine Learning, vol. 20(3), pp. 273–297, 1995.

Crotti M., Dusi M., Gringoli F., Salgarelli L.: Traffic Classification Through Sim- ple Statistical Fingerprinting. In: SIGCOMM Comput. Commun. Rev., vol. 37(1), pp. 5–16, 2007.

Draper-Gil G., Lashkari A.H., Islam-Mamun M.S., Ghorbani A.A.: Characteri- zation of Encrypted and VPN Traffic using Time-related Features. In: Charac- terization of Encrypted and VPN Traffic using Time-related Features, ICISSP. 2016.

Finamore A., Mellia M., Meo M., Rossi D.: KISS: Stochastic Packet Inspec- tion Classifier for UDP Traffic. In: IEEE/ACM Transactions on Networking, vol. 18(5), pp. 1505–1515, 2010.

Gubbi J., Buyya R., Marusic S., Palaniswami M.: Int. of Things (IoT): A vision,arch. elements, and fut. directions. In: Future Generation Computer Systems, Elsevier, vol. 29(7), pp. 1645 – 1660, 2013.

Jouet S., Perkins C., Pezaros D.: OTCP: SDN-managed congestion control for data center networks. In: OTCP: SDN-managed congestion control for data center networks, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Man- agement Symposium, pp. 171–179. 2016.

Kim H., Claffy K., Fomenkov M., Barman D., Faloutsos M., Lee K.: Internet Traffic Classification Demystified: Myths, Caveats, and the Best Practices. In: Proceedings of the 2008 ACM CoNEXT Conference, CoNEXT ’08, pp. 11:1–11:12. ACM, 2008.

Lee I., Lee K.: The Internet of Things (IoT): Applications, investments, and challenges for enterprises. In: The Internet of Things (IoT): Applications, in- vestments, and challenges for enterprises, Business Horizons, vol. 58(4), pp.431–440, 2015. ISSN0007-6813.URL http://dx.doi.org/https://doi.org/10. 1016/j.bushor.2015.03.008.

Li X., Freedman M.J.: Scaling IP Multicast on Datacenter Topologies. In: Scaling IP Multicast on Datacenter Topologies, Proc. of the 9th ACM Conf. on Emerging Networking Experiments and Technologies, pp. 61–72. 2013.

Mechtri M., Houidi I., Louati W., Zeghlache D.: SDN for Inter Cloud Networking. In: SDN for Inter Cloud Networking, 2013 IEEE SDN for Future Networks and Services (SDN4FNS), pp. 1–7. 2013.

Moore A., Zuev D., Crogan M.: Packet Classification Algorithms: From Theory to Practice. In: Packet Classification Algorithms: From Theory to Practice, Department of Computer Science Research Reports,Queen Mary, Univeristy of London, pp. 1–16. 2013.

Moore A.W., Papagiannaki K.: Toward the Accurate Identification of Network Applications. In: PAM. 2005.

Petri I., Zou M., Zamani A.R., Diaz-Montes J., Rana O., Parashar M.: Inte- grating Software Defined Networks within a Cloud Federation. In: Integrating Software Defined Networks within a Cloud Federation, 15th IEEE/ACM Interna- tional Symposium on Cluster, Cloud and Grid Computing, pp. 179–188. 2015.

Qi Y., Xu L., Yang B., Xue Y., Li J.: Discriminators for use in flow-based classi- fication. In: Discriminators for use in flow-based classification,IEEE INFOCOM 2009, pp. 648–656. 2009.

RIFAI M.: Next-Generation SDN Based Networks. In: PhD Thesis, pp. 1–178, 2017.

Salman O., Nunes B., Mayoral A., Jararweh Y., Abdelmoniem: IoT survey: An SDN and fog computing perspective. In: Computer Networks, vol. 143(6), pp. 221–246, 2018.

Sen S., Spatscheck O., Wang D.: Accurate, Scalable In-network Identification of P2P Traffic Using Application Signatures. In: Accurate, Scalable In-network Identification of P2P Traffic Using Application Signatures, Proceedings of the 13th International Conference on World Wide Web, WWW ’04, pp. 512–521. 2004.

Son J., Buyya R.: A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing. In: A Taxonomy of Software-Defined Networking (SDN)- Enabled Cloud Computing, ACM Comput. Surv., vol. 51(3), pp. 59:1–59:36, 2018. ISSN 0360-0300.

Wang W., Zhu M., Wang J., Zeng X., Yang Z.: End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In: End-to-end encrypted traffic classification with one-dimensional convolution neural networks, 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 43–48. 2017.

Wang X., Parish D.J.: Optimised Multi-stage TCP Traffic Classifier Based on Packet Size Distributions. In: 2010 Third International Conference on Commu- nication Theory, Reliability, and Quality of Service, pp. 98–103. 2010.

Yamansavascilar B., Guvensan M.A., Yavuz A.G., Karsligil M.E.: Application identification via network traffic classification. In: Application identification via network traffic classification, 2017 International Conference on Computing, Net- working and Communications (ICNC), pp. 843–848. 2017.

Yao H., Gao P., Wang J., Zhang P., Jiang C., Han Z.: Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities. In: Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities, IEEE Internet of Things Journal, vol. 6(5), pp. 7515–7525, 2019.