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Arabian Journal for Science and Engineering

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18.01.2022 | Research Article-Computer Engineering and Computer Science

A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams

verfasst von: Manish Snehi, Abhinav Bhandari

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 8/2022

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Abstract

The concurrence of state-of-the-art Industrial 5G, Cyber-Physical Systems, Smart-Systems, Industrial Internet of Things, and Additive Manufacturing paves the next-level digital remodeling. However, the transfiguration unwittingly tailpiece an operational onus on the smart-environment operators. The multiplicity and classes of IoT devices operating in the intelligent environment are myriad. The characterization of ingress network traffic and the accurate classification of devices is necessary to efficiently manage the devices and offer cutting-edge security solutions and quality of Service (QoS). The paper addresses these challenges by offering a novel intelligent framework for traffic classification leveraging behavioral attributes of IoT traffic. The paper’s contributions to the research community are fourfold. Firstly, the paper proposes a novel IoT classification framework based on Stack-Ensemble for real-time high-volume IoT traffic. The experimental results indicate that the proposed novel Stack Ensemble model can extract the best out of base models and demonstrate an accuracy of 99.94%. The intelligent models are evaluated over multiple dimensions to project the isometric view of the model performance and the experimental results. To achieve that goal, all the performance metrics that most researchers most often miss have been elucidated. Secondly, the paper comprehends the flow-level statistical characteristics of IoT devices. Third, the paper offers the distributed, scalable, and portable framework architecture. The architecture is horizontally scalable, distributing the computational load. The framework offers an end-to-end industry-grade machine-learning pipeline and triumphs the vulnerabilities of existing solutions. Finally, the paper discusses the statistical insights into the intelligent model and the results of the experimentation study. The proposed work paves the opportunities for researchers, smart-environment operators, and developers to unfold the architecture and supplement the security solutions against cyber-attacks.

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The dataset is accessible at the following URL: https://iotanalytics.unsw.edu.au/iottraces.

Ahrenholz, J.: Comparison of CORE network emulation platforms. In: 2010-MILCOM 2010 Military Communications Conference, pp. 166–171. IEEE (2010). https://doi.org/10.1109/MILCOM.2010.5680218

Aksoy, A., Gunes, M.H.: Automated IoT Device Identification using Network Traffic. IEEE International Conference on Communications 2019-May (2019). https://doi.org/10.1109/ICC.2019.8761559

Alippi, C., Ozawa, S.: Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things. In: Artificial Intelligence in the Age of Neural Networks and Brain Computing, pp. 245–263. Elsevier (2019). https://doi.org/10.1016/B978-0-12-815480-9.00012-8

Ammar, N., Noirie, L., Tixeuil, S.: Autonomous Identification of IoT Device Types based on a Supervised Classification. IEEE International Conference on Communications 2020-June (2020). https://doi.org/10.1109/ICC40277.2020.9148821

Apache Software Foundation: Apache Kafka. (Accessed on 2021-05-28) (2017). https://kafka.apache.org/

Bai, L., Yao, L., Kanhere, S.S., Wang, X., Yang, Z.: Automatic device classification from network traffic streams of internet of things. In: 2018 IEEE 43rd conference on local computer networks (LCN), pp. 1–9. IEEE (2018)

Baker, F., Foster, B., Sharp, C.: Cisco Architecture for Lawful Intercept in IP Networks-RFC3924. (Accessed on 2021-05-20) (2004). https://datatracker.ietf.org/doc/html/rfc3924

Bao, J., Hamdaoui, B., Wong, W.K.: IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Security. 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 pp. 565–570 (2020). https://doi.org/10.1109/IWCMC48107.2020.9148110

Chamola, V.; Hassija, V.; Gupta, V.; Guizani, M.: A comprehensive review of the COVID-19 pandemic and the role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access 8(April), 90225–90265 (2020). https://doi.org/10.1109/ACCESS.2020.2992341CrossRef

10.

Cirillo, G.; Passerone, R.: Packet length spectral analysis for IoT flow classification using ensemble learning. IEEE Access 8, 138616–138641 (2020). https://doi.org/10.1109/ACCESS.2020.3012203CrossRef

11.

Čolaković, A.; Hadžialić, M.: Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Comput. Netw. 144, 17–39 (2018). https://doi.org/10.1016/j.comnet.2018.07.017CrossRef

12.

Dainotti, A.; Pescapé, A.; Claffy, K.C.: Issues and future directions in traffic classification. IEEE Netw. 26(1), 35–40 (2012). https://doi.org/10.1109/MNET.2012.6135854CrossRef

13.

Desai, B.A., Divakaran, D.M., Nevat, I., Peter, G.W., Gurusamy, M.: A feature-ranking framework for IoT device classification. 2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019 2061, 64–71 (2019). https://doi.org/10.1109/COMSNETS.2019.8711210

14.

DEVNET, C.: Cisco DevNet Code Exchange: Discover code repositories related to Cisco technologies. (Accessed on 2021-05-30) (2016). https://developer.cisco.com/codeexchange/github/repo/cisco/joy/

15.

Dulhare, U.N., Mubeen, A., Ahmad, K.: Hands On H2O Machine Learning Tool. In: Machine Learning and Big Data, pp. 423–453. Wiley (2020). https://doi.org/10.1002/9781119654834.ch15

16.

Guo, H.; Heidemann, J.: Detecting IoT devices in the internet. IEEE/ACM Trans. Netw. 28(5), 2323–2336 (2020). https://doi.org/10.1109/TNET.2020.3009425CrossRef

17.

Hamza, A., Ranathunga, D., Gharakheili, H.H., Roughan, M., Sivaraman, V.: Clear as MUD: Generating, validating and applying IoT behavioral profiles. IoT S and P 2018-Proceedings of the 2018 Workshop on IoT Security and Privacy, Part of SIGCOMM 2018 pp. 8–14 (2018). https://doi.org/10.1145/3229565.3229566

18.

Holst, A.: Internet of Things (IoT) connected devices worldwide from 2019 to 2030. (Accessed on 2021-05-12) (2021). https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/

19.

Hsu, A., Tront, J., Raymond, D., Wang, G., Butt, A.: Automatic IoT Device Classification using Traffic Behavioral Characteristics. Conference Proceedings-IEEE SOUTHEASTCON 2019-April, 1–7 (2019). https://doi.org/10.1109/SoutheastCon42311.2019.9020640

20.

Izonin, I., Tkachenko, R., Vitynskyi, P., Zub, K., Tkachenko, P., Dronyuk, I.: Stacking-based GRNN-SGTM Ensemble Model for Prediction Tasks. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 326–330. IEEE (2020). https://doi.org/10.1109/DASA51403.2020.9317124

21.

Khadse, V.; Mahalle, P.N.; Shinde, G.R.: A novel approach of ensemble learning with feature reduction for classification of binary and multiclass IoT data. Turkish J. Comput. Math. Educ. (TURCOMAT) 12(6), 2072–2083 (2021). https://doi.org/10.17762/turcomat.v12i6.4811CrossRef

22.

Khandait, P.; Hubballi, N.; Mazumdar, B.: IoTHunter: IoT network traffic classification using device specific keywords. IET Netw. 10(2), 59–75 (2021). https://doi.org/10.1049/ntw2.12007CrossRef

23.

Kraus, M.; Feuerriegel, S.; Oztekin, A.: Deep learning in business analytics and operations research: Models, applications and managerial implications. Eur. J. Op. Res. 281(3), 628–641 (2020). https://doi.org/10.1016/j.ejor.2019.09.018CrossRef

24.

Laner, M.; Nikaein, N.; Svoboda, P.; Popovic, M.; Drajic, D.; Krco, S.: Traffic models for machine-to-machine (M2M) communications. Elsevier Ltd, New York (2015). https://doi.org/10.1016/b978-1-78242-102-3.00008-3CrossRef

25.

Lear, E., Droms, R., Romascanu, D.: Manufacturer Usage Description Specification. (Accessed on 2021-05-23) (2018). https://tools.ietf.org/id/draft-ietf-opsawg-mud-22.html

26.

Lee, J.; Bagheri, B.; Kao, H.A.: A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015). https://doi.org/10.1016/j.mfglet.2014.12.001CrossRef

27.

Lemley, J.; Bazrafkan, S.; Corcoran, P.: Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electron. Mag. 6(2), 48–56 (2017). https://doi.org/10.1109/MCE.2016.2640698CrossRef

28.

Lim, Y.s., Kim, H.c., Jeong, J., Kim, C.k., Kwon, T.T., Choi, Y.: Internet traffic classification demystified. In: Proceedings of the 6th International COnference on-Co-NEXT-10, p. 1. ACM Press (2010). https://doi.org/10.1145/1921168.1921180

29.

Marchal, S.; Miettinen, M.; Nguyen, T.D.; Sadeghi, A.R.; Asokan, N.: AuDI: Toward autonomous IoT device-type identification using periodic communication. IEEE J. Selected Areas Commun. 37(6), 1402–1412 (2019). https://doi.org/10.1109/JSAC.2019.2904364CrossRef

30.

Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: IoT Devices Recognition through Object Detection and Classification Techniques. In: 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), pp. 12–20. IEEE (2019). https://doi.org/10.1109/WorldS4.2019.8903926

31.

Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, J.D., Ochoa, M., Tippenhauer, N.O., Elovici, Y.: ProfilIoT: A Machine Learning Approach for IoT Device Identification Based on Network Traffic Analysis. In: Proceedings of the Symposium on Applied Computing, vol. Part F1280, pp. 506–509. ACM (2017). https://doi.org/10.1145/3019612.3019878

32.

Mendes Junior, J.J.A., Freitas, M.L., Siqueira, H.V., Lazzaretti, A.E., Pichorim, S.F., Stevan, S.L.: Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed. Signal Process. Control 59, 101920 (2020). https://doi.org/10.1016/j.bspc.2020.101920

33.

Mills, D., Delaware, U., J. Martin, E., Burbank, J., Kasch, W.: RFC 5905-Network Time Protocol Version 4: Protocol and Algorithms Specification. (Accessed on 2021-06-10) (2010). https://datatracker.ietf.org/doc/html/rfc5905

34.

Moore, A.W.; Zuev, D.: Internet traffic classification using bayesian analysis techniques. Perform. Eval. Rev. 33(1), 50–60 (2005). https://doi.org/10.1145/1071690.1064220CrossRef

35.

Ndiaye, M.; Oyewobi, S.S.; Abu-Mahfouz, A.M.; Hancke, G.P.; Kurien, A.M.; Djouani, K.: IoT in the wake of COVID-19: a survey on contributions. Challenges and Evolution. IEEE Access 8, 186821–186839 (2020). https://doi.org/10.1109/ACCESS.2020.3030090CrossRef

36.

Nguyen, T.T.; Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surveys Tutor. 10(4), 56–76 (2008). https://doi.org/10.1109/SURV.2008.080406CrossRef

37.

Pacheco, F.; Exposito, E.; Gineste, M.; Baudoin, C.; Aguilar, J.: Towards the deployment of machine learning solutions in network traffic classification: a systematic survey. IEEE Commun. Surveys Tutor. 21(2), 1988–2014 (2019). https://doi.org/10.1109/COMST.2018.2883147CrossRef

38.

Reynolds, J., Postel, J.: ASSIGNED NUMBERS. (Accessed on 2021-05-23) (1992). https://datatracker.ietf.org/doc/html/rfc1340

39.

Santos, M.R., Andrade, R.M., Gomes, D.G., Callado, A.C.: An efficient approach for device identification and traffic classification in IoT ecosystems. Proceedings-IEEE Symposium on Computers and Communications 2018-June, 304–309 (2018). https://doi.org/10.1109/ISCC.2018.8538630

40.

Shafiq, M., Yu, X., Laghari, A.A., Yao, L., Karn, N.K., Abdessamia, F.: Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), vol. 10, pp. 2451–2455. IEEE (2016). https://doi.org/10.1109/CompComm.2016.7925139

41.

Sisinni, E.; Saifullah, A.; Han, S.; Jennehag, U.; Gidlund, M.: Industrial Internet of Things: challenges, opportunities and directions. IEEE Trans. Ind. Inf. 14(11), 4724–4734 (2018). https://doi.org/10.1109/TII.2018.2852491CrossRef

42.

Sivanathan, A., Gharakheili, H.H.: IoT Security-IoT Traffic Analysis. (Accessed on 2021-05-26) (2018). https://iotanalytics.unsw.edu.au/iottraces

43.

Sivanathan, A.; Gharakheili, H.H.; Loi, F.; Radford, A.; Wijenayake, C.; Vishwanath, A.; Sivaraman, V.: Classifying IoT devices in smart environments using network traffic characteristics. IEEE Trans. Mobile Comput. 18(8), 1745–1759 (2019). https://doi.org/10.1109/TMC.2018.2866249CrossRef

44.

Sivanathan, A., Gharakheili, H.H., Sivaraman, V.: Can We Classify an IoT Device using TCP Port Scan? 2018 IEEE 9th International Conference on Information and Automation for Sustainability, ICIAfS 2018 (2018). https://doi.org/10.1109/ICIAFS.2018.8913346

45.

Sivanathan, A., Sherratt, D., Gharakheili, H.H., Radford, A., Wijenayake, C., Vishwanath, A., Sivaraman, V.: Characterizing and classifying IoT traffic in smart cities and campuses. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), vol. 18, pp. 559–564. IEEE (2017). https://doi.org/10.1109/INFCOMW.2017.8116438

46.

Snehi, J.; Bhandari, A.; Baggan, V.; Snehi, M.: Diverse methods for signature based intrusion detection schemes adopted. Int. J. Recent Technol. Eng. (IJRTE) 9(2), 44–49 (2020)CrossRef

47.

Snehi, J., Bhandari, A., Snehi, M., Tandon, U., Baggan, V.: Global intrusion detection environments and platform for anomaly-based intrusion detection systems. In: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, pp. 817–831. Springer (2021)

48.

Snehi, M., Bhandari, A.: Apprehending Mirai Botnet Philosophy and Smart Learning Models for IoT-DDoS Detection. In: 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 501–505 (2021). https://doi.org/10.1109/INDIACom51348.2021.00089

49.

Snehi, M.; Bhandari, A.: Vulnerability retrospection of security solutions for software-defined cyber-physical system against DDOS and IoT-DDOS attacks. Comput. Sci. Rev. (2021). https://doi.org/10.1016/j.cosrev.2021.100371CrossRef

50.

Tkachenko, R.; Izonin, I.; Dronyuk, I.; Logoyda, M.; Tkachenko, P.: Recovery of missing sensor data with GRNN-based cascade scheme. Int. J. Sensors, Wireless Commun. Control 11(5), 531–541 (2021). https://doi.org/10.2174/2210327910999200813151904CrossRef

51.

Trevisan, M., Drago, I., Mellia, M., Munafo, M.M.: Towards web service classification using addresses and DNS. In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 38–43. IEEE (2016). https://doi.org/10.1109/IWCMC.2016.7577030

52.

Verma, J., Bhandari, A., Singh, G.: A meta-analysis of role of network intrusion detection systems in confronting network attacks. In: 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 506–511 (2021). https://doi.org/10.1109/INDIACom51348.2021.00090

53.

Weiss, M.A.; Eidson, J.; Barry, C.; Broman, D.; Iannucci, B.; Lee, E.A.; Stanton, K.; Sr., Goldin, L.: Time-aware applications, computers and communication systems (TAACCS). Tech. rep. (2015). https://doi.org/10.6028/NIST.TN.1867

54.

Yu, T., Sekar, V., Seshan, S., Agarwal, Y., Xu, C.: Handling a trillion (unfixable) flaws on a billion devices. In: Proceedings of the 14th ACM Workshop on Hot Topics in Networks, pp. 1–7. ACM (2015). https://doi.org/10.1145/2834050.2834095

Titel: A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams
verfasst von: Manish Snehi
Abhinav Bhandari
Publikationsdatum: 18.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 8/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-06472-z

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