International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

StreamSafe: Improving QoS and Security in IoT Networks

Sathiamoorthy A.Dept. of Information Technology, Sri Lanka Institute of Information Technology, Colombo, Sri LankaMithusan S.Dept. of Information Technology, Sri Lanka Institute of Information Technology, Colombo, Sri LankaRathnayaka R.M.L.R.Dept. of Information Technology, Sri Lanka Institute of Information Technology, Colombo, Sri LankaKajenthiran S.Dept. of Information Technology, Sri Lanka Institute of Information Technology, Colombo, Sri LankaMahaadikara M.D.J.T. HansikaDept. of Computer Systems Engineering, Sri Lanka Institute of Information Technology, Colombo, Sri LankaDinithi PandithageDept. of Computer Systems Engineering, Sri Lanka Institute of Information Technology, Colombo, Sri Lanka

Vol 7 No 11 (2023): Volume 7, Issue 11, November 2023 | Pages: 170-176

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 08-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.711024

pdf iconFull Text PDF
Abstract

This research project takes a holistic strategy to improve QoS and security in SDN-based IoT networks. We offer an intricate solution with a federated autoencoder-based tool to precisely identify malicious network traffic in response to the expanding IoT ecosystem and its security issues. We use a QoS dashboard coupled with OpenDaylight to monitor latency, throughput, packet loss, and jitter, dynamic resource allocation based on device priorities, and better MQTT AES encryption. These elements constitute a framework for SDN-based IoT networks' changing needs. Global autoencoder models with client models on IoT devices provide real-time anomaly detection in our system. We offer a packet manipulator to generate tailored network traffic samples for robust model training. The OpenDaylight-integrated QoS dashboard gives administrators real-time network performance data. A priority-based resource allocation system allocates resources by device significance to preserve QoS during peak demand. AES encryption for MQTT, a popular IoT standard, is also improved. It protects critical IoT data from security risks. Our Mininet-based Ubuntu simulations show that our technique maintains QoS, detects network anomalies, and strengthens IoT network security. These findings show that our methodology may improve SDN-based IoT deployment reliability and security, creating a more resilient and secure ecosystem.

Keywords

Internet of Things (IoT), Quality of Service (QoS), Software-Defined Networking (SDN), Anomaly Detection, OpenDaylight, Dynamic Resource Allocation, AES, MQTT.


Citation of this Article

Sathiamoorthy A., Mithusan S., Rathnayaka R.M.L.R., Kajenthiran S., Mahaadikara M.D.J.T. Hansika, Dinithi Pandithage, “StreamSafe: Improving QoS and Security in IoT Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 11, pp 170-176, November 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.711024

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. H. Moni Sree, R. Pavithra, R. B. Nithyaa Shri, and M. Shanthi, “Review on Iot Security and Its Real-Time Application,” 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA, 2021.
  2. A.Abdullah, R. Hamad, M. Abdulrahman, H. Moala, and S. Elkhediri, “CyberSecurity: A Review of Internet of Things (IoT) Security Issues, Challenges and Techniques,” 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, May 2019.
  3. K. Sharma and R. Nandal, “A literature study on machine learning fusion with IoT,” Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, vol. 2019-April, pp. 1440–1445, Apr. 2019.
  4. T. M. Booij, I. Chiscop, E. Meeuwissen, N. Moustafa, and F. T. H. D. Hartog, “ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets,” IEEE Internet Things J, vol. 9, no. 1, pp. 485–496, Jan. 2022.
  5. K. H. Manguri and S. M. Omer, “SDN for IoT Environment: A Survey and Research Challenges,” ITM Web of Conferences, vol. 42, p. 01005, 2022.
  6. W. Rafique, L. Qi, I. Yaqoob, M. Imran, R. U. Rasool, and W. Dou, “Complementing IoT Services through Software Defined Networking and Edge Computing: A Comprehensive Survey,” IEEE Communications Surveys and Tutorials, vol. 22, no. 3, pp. 1761–1804, Jul. 2020.
  7. A.Dvornikov, P. Abramov, S. Efremov, and L. Voskov, “QoS Metrics Measurement in Long Range IoT Networks,” Proceedings - 2017 IEEE 19th Conference on Business Informatics, CBI 2017, vol. 2, pp. 15–20, Aug. 2017.
  8. V. K. Quy, V. H. Nam, D. M. Linh, N. T. Ban, and N. D. Han, “A Survey of QoS-aware Routing Protocols for the MANET-WSN Convergence Scenarios in IoT Networks,” Wirel Pers Commun, vol. 120, no. 1, pp. 49–62, Sep. 2021.
  9. V. Gugueoth, S. Safavat, and S. Shetty, “Security of Internet of Things (IoT) using federated learning and deep learning — Recent advancements, issues and prospects,” ICT Express, Mar. 2023.
  10. S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges,” IEEE Communications Magazine, vol. 58, no. 6, pp. 46–51, Jun. 2020.
  11. V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems, vol. 115, pp. 619–640, Feb. 2021.
  12. B. Cetin, A. Lazar, J. Kim, A. Sim, and K. Wu, “Federated Wireless Network Intrusion Detection,” Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, pp. 6004–6006, Dec. 2019.
  13. T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A. R. Sadeghi, “DÏoT: A federated self-learning anomaly detection system for IoT,” Proc Int Conf Distrib Comput Syst, vol. 2019-July, pp. 756–767, Jul. 2019.
  14. D. Attota, V. Mothukuri, R. Parizi, S. P.-I. Access, and undefined 2021, “An ensemble multi-view federated learning intrusion detection for IoT,” ieeexplore.ieee.org, Accessed: May 03, 2023.
  15. O. Friha, M. Ferrag, L. Shu, … L. M.-J. of P. and, and undefined 2022, “FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things,” Elsevier, Accessed: May 03, 2023.
  16. Y. Zhao, J. Chen, D. Wu, J. Teng, and S. Yu, “Multi-task network anomaly detection using federated learning,” ACM International Conference Proceeding Series, pp. 273–279, Dec. 2019.
  17. Z. Lian and C. Su, “Decentralized Federated Learning for Internet of Things Anomaly Detection,” ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security, pp. 1249–1251, May 2022.
  18. A.Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys and Tutorials, vol. 17, no. 4, pp. 2347–2376, Oct. 2015.
  19. B. K. Mukherjee, S. I. Pappu, M. J. Islam, and U. K. Acharjee, “An SDN based distributed IoT network with NFV implementation for smart cities,” Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 325 LNICST, pp. 539–552, 2020.
  20. J. R. Naif, G. H. Abdul-Majeed, and A. K. Farhan, “Secure IOT System Based on Chaos-Modified Lightweight AES,” 2019 International Conference on Advanced Science and Engineering, ICOASE 2019, pp. 12–17, Apr. 2019.
  21. F. Dewanta, B. Y. Yustiarini, B. Indrakusumo, and R. Harsritanto, “A study of secure communication scheme in MQTT: TLS vs AES cryptography,” JURNAL INFOTEL, vol. 14, no. 4, pp. 269–276, Nov. 2022.
  22. J. Ahamed, M. Zahid, M. Omar, and K. Ahmad, “AES and MQTT based security system in the internet of things,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 8, pp. 1589–1598, Nov. 2019.
  23. F. B. Setiawan and Magfirawaty, “Securing Data Communication Through MQTT Protocol with AES-256 Encryption Algorithm CBC Mode on ESP32-Based Smart Homes,” 2021 International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2021, pp. 166–170, 2021.
  24. C. Okur, A. Orman, and M. Dener, “DDOS Intrusion Detection with Machine Learning Models: N-BaIoT Data Set,” pp. 607–619, 2022.
  25. G. Pang, C. Shen, L. Cao, and A. Van Den Hengel, “Deep Learning for Anomaly Detection,” ACM Computing Surveys (CSUR), vol. 54, no. 2, Mar. 2021.
  26. G. D’Angelo and F. Palmieri, “Network traffic classification using deep convolutional recurrent auto encoder neural networks for spatial–temporal features extraction,” Journal of Network and Computer Applications, vol. 173, Jan. 2021.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.