Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Survey of Traffic Classification Solution in IoT Networks

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Rami J. Alzahrani, Ahmed Alzahrani

Rami J Alzahrani and Ahmed Alzahrani. Survey of Traffic Classification Solution in IoT Networks. International Journal of Computer Applications 183(9):37-45, June 2021. BibTeX

	author = {Rami J. Alzahrani and Ahmed Alzahrani},
	title = {Survey of Traffic Classification Solution in IoT Networks},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {9},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {37-45},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2021921392},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Internet of Things (IoT) is creating a new evolution in the present and future Internet. The idea of IoT is to establish transmission capacities using a ubiquitous, distributed and diverse gadgets network. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. The increasing numbers of IoT devices and diverse IoT traffic patterns has created the need for traffic classification methods to provide solutions for IoT applications’issues. Although it has been presented in many papers and surveys, network traffic classification is still undeveloped well in IoT because of the variations in traffic classifications in IoT and NonIoT gadgets. This paper discusses the arising patterns of IoT network traffic classifications and putting them in practical use. It also presents an overview of traditional traffic classification methods, as well as a discussion with a categorization.This paper evaluated the performance metrics such as accuracy, recall, precision and F1 score for these Machine Learning algorithms: Decision Tree (DT), K-Nearest Neighbors (K-NN), Naïve Bayes (NB) and Gradient Boosting (GRB) classifiers. The analysis of normal and attack traffic is done by using WEKA software tools and by utilizing the BoT-IoT dataset [1].


  1. N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,” Future Generation Computer Systems, vol. 100, pp. 779–796, Nov. 2019, doi: 10.1016/j.future.2019.05.041.
  2. “Cisco Visual Networking Index: Forecast and Trends, 2017–2022,” 2019.
  3. O. Garcia-Morchon, S. Kumar, and M. Sethi, “Internet of Things (IoT) Security: State of the Art and Challenges,” Apr. 2019. doi: 10.17487/RFC8576.
  4. A. Sabella, R. Irons-Mclean, and M. Yannuzzi, Orchestrating and Automating Security for the Internet of Things: Delivering Advanced Security Capabilities from Edge to Cloud for IoT. 2018.
  5. M. R. Shahid, G. Blanc, Z. Zhang, and H. Debar, “IoT Devices Recognition Through Network Traffic Analysis,” in Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Jan. 2019, pp. 5187–5192, doi: 10.1109/BigData.2018.8622243.
  6. S. P. Khedkar and R. Aroulcanessane, “SDN enabled cloud, IoT and DCNs: A comprehensive Survey,” Sep. 2019, doi: 10.1109/ICCUBEA47591.2019.9129091.
  7. S. P. Khedkar and R. AroulCanessane, “Machine Learning Model for classification of IoT Network Traffic,” Nov. 2020, pp. 166–170, doi: 10.1109/i-smac49090.2020.9243468.
  8. F. Tang, Z. M. Fadlullah, B. Mao, and N. Kato, “An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5141–5154, Dec. 2018, doi: 10.1109/JIOT.2018.2838574.
  9. “Discriminators for use in flow-based classification,” 2013. (accessed Jan. 01, 2021).
  10. Y. Qi, L. Xu, B. Yang, Y. Xue, and J. Li, “Packet classification algorithms: From theory to practice,” in Proceedings - IEEE INFOCOM, 2009, pp. 648–656, doi: 10.1109/INFCOM.2009.5061972.
  11. M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lloret, “Network Traffic Classifier with Convolutional and Recurrent Neural Networks for Internet of Things,” IEEE Access, vol. 5, pp. 18042–18050, Sep. 2017, doi: 10.1109/ACCESS.2017.2747560.
  12. M. Shafiq, X. Yu, A. A. Laghari, L. Yao, N. K. Karn, and F. Abdessamia, “Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms,” in 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, May 2017, pp. 2451–2455, doi: 10.1109/CompComm.2016.7925139.
  13. I. P. Possebon, A. S. Silva, L. Z. Granville, A. Schaeffer-Filho, and A. Marnerides, “Improved Network Traffic Classification Using Ensemble Learning,” in Proceedings - IEEE Symposium on Computers and Communications, Jun. 2019, vol. 2019-June, doi: 10.1109/ISCC47284.2019.8969637.
  14. I. Kotenko, I. Saenko, A. Kushnerevich, and A. Branitskiy, “Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach,” in Proceedings - 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2019, Mar. 2019, pp. 340–347, doi: 10.1109/EMPDP.2019.8671571.
  15. S. Rezvy, Y. Luo, M. Petridis, A. Lasebae, and T. Zebin, “An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks,” Apr. 2019, doi: 10.1109/CISS.2019.8693059.
  16. “Hacked cameras, DVRs powered today’s massive internet outage. Krebson Security - Google Search.” .
  17. A. Kumar and T. J. Lim, “Early Detection OfMirai-Like IoT Bots In Large-Scale Networks Through Sub-Sampled Packet Traffic Analysis,” Lecture Notes in Networks and Systems, vol. 70, pp. 847–867, Jan. 2019.
  18. I. Hafeez, A. Y. Ding, M. Antikainen, and S. Tarkoma, “Real-time IoT device activity detection in edge networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Aug. 2018, vol. 11058 LNCS, pp. 221–236, doi: 10.1007/978-3-030-02744-5_17.
  19. J. Shen, Yi. Li, B. Li, H. Chen, and J. Li, “IoT Eye An Efficient System for Dynamic IoT Devices Auto-discovery on Organization Level,” in Proceedings - 4th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017, Jul. 2017, pp. 294–299, doi: 10.1109/CSCloud.2017.66.
  20. Y. Meidanet al., “ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis,” in Proceedings of the ACM Symposium on Applied Computing, Apr. 2017, vol. Part F128005, pp. 506–509, doi: 10.1145/3019612.3019878.
  21. J. Ortiz, C. Crawford, and F. Le, “DeviceMien: Network device behavior modeling for identifying unknown IoT devices,” in IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation, Apr. 2019, pp. 106–117, doi: 10.1145/3302505.3310073.
  22. M. R. P. Santos, R. M. C. Andrade, D. G. Gomes, and A. C. Callado, “An efficient approach for device identification and traffic classification in IoT ecosystems,” in Proceedings - IEEE Symposium on Computers and Communications, Nov. 2018, vol. 2018-June, pp. 304–309, doi: 10.1109/ISCC.2018.8538630.
  23. M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. R. Sadeghi, and S. Tarkoma, “IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT,” in Proceedings - International Conference on Distributed Computing Systems, Jul. 2017, pp. 2177–2184, doi: 10.1109/ICDCS.2017.283.
  24. M. Miettinen et al., “IoT Sentinel Demo: Automated Device-Type Identification for Security Enforcement in IoT,” in Proceedings - International Conference on Distributed Computing Systems, Jul. 2017, pp. 2511–2514, doi: 10.1109/ICDCS.2017.284.
  25. A. Sivanathanet al., “Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics,” IEEE Transactions on Mobile Computing, vol. 18, no. 8, pp. 1745–1759, Aug. 2019, doi: 10.1109/TMC.2018.2866249.
  26. L. Bai, L. Yao, S. S. Kanhere, X. Wang, and Z. Yang, “Automatic Device Classification from Network Traffic Streams of Internet of Things,” in Proceedings - Conference on Local Computer Networks, LCN, Feb. 2019, vol. 2018-October, pp. 597–605, doi: 10.1109/LCN.2018.8638232.
  27. “Manufacturer Usage Description Specification,” 2019. (accessed Dec. 30, 2020).
  28. A. Hamza, D. Ranathunga, H. H. Gharakheili, M. Roughan, and V. Sivaraman, “Clear as MUD: Generating, validating and applying IoT behavioral profiles,” in IoT S and P 2018 - Proceedings of the 2018 Workshop on IoT Security and Privacy, Part of SIGCOMM 2018, Aug. 2018, vol. 18, pp. 8–14, doi: 10.1145/3229565.3229566.
  29. A. Hamza, D. Ranathunga, H. H. Gharakheili, T. A. Benson, M. Roughan, and V. Sivaraman, “Verifying and Monitoring IoTs Network Behavior using MUD Profiles,” arXiv, Feb. 2019.
  30. Y. Ashibani and Q. H. Mahmoud, “A User Authentication Model for IoT Networks Based on App Traffic Patterns,” in 2018 IEEE 9th AnnualInformation Technology, Electronics and Mobile Communication Conference, IEMCON 2018, Jan. 2019, pp. 632–638, doi: 10.1109/IEMCON.2018.8614892.
  31. S. H. Kim and D. I. Kim, “Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks,” IEEE Transactions on Mobile Computing, vol. 19, no. 7, pp. 1731–1744, Jul. 2020, doi: 10.1109/TMC.2019.2913386.
  32. H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, and Z. Han, “Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7515–7525, Oct. 2019, doi: 10.1109/JIOT.2019.2901348.
  33. H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang, and S. Xiao, “Data gathering with compressive sensing in wireless sensor networks: A random walk based approach,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 35–44, Jan. 2015, doi: 10.1109/TPDS.2014.2308212.
  34. S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic Routing Between Capsules,” 2017. doi: 10.5555/3294996.3295142.
  35. F. Sallabi, F. Naeem, M. Awad, and K. Shuaib, “Managing IoT-Based Smart Healthcare Systems Traffic with Software Defined Networks,” Nov. 2018, doi: 10.1109/ISNCC.2018.8530920.


IoT, Security, Networks, Traffic Classification, IoT security