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VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2027))

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Abstract

The adoption of 5G networks is seen as an enabler for IoT. With low latency and increased reach, many more IoT devices can now be connected and controlled. An increase in the number of IoT devices since its inception brought forth the need for a lightweight communication protocol. The protocol in question should be able to cater to a large number of IoT devices. These requirements were the basis of the MQTT protocol. Attackers can utilize the protocol to target a network. Furthermore it is a herculean task to manually identify an attack in a huge network. Artificial Intelligence can be used to efficiently detect such attacks with a high degree of accuracy. In this research we propose a hybrid deep learning mult-iclass classification model VAIDS. VAIDS utilizes the CNN algorithm to extract features from the dataset. These features are then utilized as inputs for the LSTM algorithm. The proposed model can detect five types of anomalies within an IoT network that uses the MQTT protocol. The proposed model is trained, tested and validated against the MQTT-IoT-IDS2020 dataset and classifies a given input into one of five attack classes. The model showcases a high degree of accuracy of 99.97%.

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References

  1. Kelly, S.D.T., Suryadevara, N.K., Mukhopadhyay, S.C.: Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sens. J. 13(10), 3846–3853 (2013)

    Article  Google Scholar 

  2. Rayes, A., Salam, S.: The things in iot: Sensors and actuators. In: Internet of Things From Hype to Reality: The Road to Digitization, pp. 63–82. Springer International Publishing, Cham (2022)

    Chapter  Google Scholar 

  3. Suresh, P., Daniel, J.V., Parthasarathy, V., Aswathy, R.H.: November. A state of the art review on the Internet of Things (IoT) history, technology and fields of deployment. In: 2014 International Conference On Science Engineering and Management Research (ICSEMR) (pp. 1–8). IEEE (2014)

    Google Scholar 

  4. Xenofontos, C., Zografopoulos, I., Konstantinou, C., Jolfaei, A., Khan, M.K., Choo, K.K.R.: Consumer, commercial, and industrial iot (in) security: attack taxonomy and case studies. IEEE Internet Things J. 9(1), 199–221 (2021)

    Article  Google Scholar 

  5. Sisinni, E., Saifullah, A., Han, S., Jennehag, U., Gidlund, M.: Industrial internet of things: challenges, opportunities, and directions. IEEE Trans. Industr. Inf. 14(11), 4724–4734 (2018)

    Article  Google Scholar 

  6. Sinha, S.: State of IOT 2023: Number of connected IOT devices growing 16% to 16.7 billion globally, IoT Analytics (2023). https://iot-analytics.com/number-connected-iot-devices/. Accessed 27 Aug 2023

  7. Soni, D., Makwana, A.: April. A survey on mqtt: a protocol of internet of things (iot). In: International Conference on Telecommunication, Power Analysis and Computing Techniques (ICTPACT-2017) vol. 20, pp. 173–177 (2017)

    Google Scholar 

  8. Boyd, B., et al.: Building Real-time Mobile Solutions with MQTT and IBM MessageSight. IBM Redbooks (2014)

    Google Scholar 

  9. Hunkeler, U., Truong, H.L., Stanford-Clark, A.: January. MQTT-S—A publish/subscribe protocol for Wireless Sensor Networks. In: 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE’08), pp. 791–798 IEEE (2008)

    Google Scholar 

  10. Tang, K., Wang, Y., Liu, H., Sheng, Y., Wang, X., Wei, Z.: October. Design and implementation of push notification system based on the MQTT protocol. In 2013 International Conference on Information Science and Computer Applications (ISCA 2013), pp. 116–119. Atlantis Press (2013)

    Google Scholar 

  11. Standard, O.A.S.I.S.: MQTT Version 5.0, vol. 22, p. 2020 (2019)

    Google Scholar 

  12. Nazir, S., Kaleem, M.: March. Reliable image notifications for smart home security with MQTT. In: 2019 International Conference on Information Science and Communication Technology (ICISCT), pp. 1–5. IEEE (2019)

    Google Scholar 

  13. Profanter, S., Tekat, A., Dorofeev, K., Rickert, M. and Knoll, A., 2019, February. OPC UA versus ROS, DDS, and MQTT: Performance evaluation of industry 4.0 protocols. In 2019 IEEE International Conference on Industrial Technology (ICIT) (pp. 955–962). IEEE

    Google Scholar 

  14. Franceschinis, M., Pastrone, C., Spirito, M.A. and Borean, C.: October. On the performance of ZigBee Pro and ZigBee IP in IEEE 802.15. 4 networks. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 83–88. IEEE (2013)

    Google Scholar 

  15. Zalewski, M.: Silence on the Wire: A Field Guide to Passive Reconnaissance and Indirect Attacks, No Starch Press (2005)

    Google Scholar 

  16. Hyder, M.F., Ismail, M.A.: Securing control and data planes from reconnaissance attacks using distributed shadow controllers, reactive and proactive approaches. IEEE Access 9, 21881–21894 (2021)

    Article  Google Scholar 

  17. Cho, J.S., Yeo, S.S., Kim, S.K.: Securing against brute-force attack: a hash-based RFID mutual authentication protocol using a secret value. Comput. Commun. 34(3), 391–397 (2011)

    Article  Google Scholar 

  18. Vishwakarma, R., Jain, A.K.: A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommun. Syst. 73(1), 3–25 (2020)

    Article  Google Scholar 

  19. Ullah, I., Mahmoud, Q.H.: January. An anomaly detection model for IoT networks based on flow and flag features using a feed-forward neural network. In: 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), pp. 363–368. IEEE (2022)

    Google Scholar 

  20. Alzahrani, A., Aldhyani, T.H.: Artificial intelligence algorithms for detecting and classifying MQTT protocol internet of things attacks. Electronics 11(22), 3837 (2022)

    Article  Google Scholar 

  21. Ullah, I., Mahmoud, Q.H.: Design and development of a deep learning-based model for anomaly detection in IoT networks. IEEE Access 9, 103906–103926 (2021)

    Article  Google Scholar 

  22. Khan, M.A., et al.: A deep learning-based intrusion detection system for MQTT enabled IoT. Sensors 21(21), 7016 (2021)

    Article  Google Scholar 

  23. Shajan, A.A.: Intrusion Detection in IoT devices using Zero Bias DNN (Doctoral dissertation, Dublin, National College of Ireland) (2021)

    Google Scholar 

  24. Hindy, H., Bayne, E., Bures, M., Atkinson, R., Tachtatzis, C., Bellekens, X.: Machine learning based IoT intrusion detection system: An MQTT case study (MQTT-IoT-IDS2020 dataset). In: Ghita, B., Shiaeles, S. (eds.) Selected Papers from the 12th International Networking Conference: INC 2020, pp. 73–84. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-64758-2_6

    Chapter  Google Scholar 

  25. Quina, A. and Jones, M.C.: SECFORCE/Sparta: network infrastructure penetration testing tool, GitHub (2020). https://github.com/SECFORCE/sparta. Accessed 27 Aug 2023

  26. Seger, C.: An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing (2018)

    Google Scholar 

  27. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020)

    Article  Google Scholar 

  28. Gholamalinezhad, H., Khosravi, H.: Pooling methods in deep neural networks, a review. arXiv preprint arXiv:2009.07485 (2020)

  29. Liang, H., Li, Q.: Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sensing 8(2), 99 (2016)

    Article  Google Scholar 

  30. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  31. Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)

    Article  Google Scholar 

  32. Zou, X., Hu, Y., Tian, Z., Shen, K.: October. Logistic regression model optimization and case analysis. In: 2019 IEEE 7th international conference on computer science and network technology (ICCSNT), pp. 135–139. IEEE (2019)

    Google Scholar 

  33. Gao, B. and Pavel, L.: On the properties of the softmax function with application in game theory and reinforcement learning. arXiv preprint arXiv:1704.00805 (2017)

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Author information

Authors and Affiliations

  1. Sardar Patel University of Police Security and Criminal Justice, Jodhpur, India

    Chetanya Kunndra, Arjun Choudhary & Prashant Mathur

  2. Indian Institute of Technology, Jodhpur, India

    Jaspreet Kaur

Authors
  1. Chetanya Kunndra
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  2. Arjun Choudhary
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  3. Jaspreet Kaur
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  4. Prashant Mathur
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Corresponding author

Correspondence to Arjun Choudhary .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. University of Derby, Derby, UK

    Aaisha Makkar

  3. University of Derby, Derby, UK

    Myra Conway

  4. Indian Institute of Information Technology, Bhopal, India

    Ashutosh K. Singh

  5. University of Clermont Auvergne, Aubière, France

    Antoine Vacavant

  6. ENSA - Cadi Ayyad University, Marrakesh, Morocco

    Anas Abou el Kalam

  7. Halmstad University, Halmstad, Sweden

    Mohamed-Rafik Bouguelia

  8. Central University of Karnataka, Kalaburagi, India

    Ravindra Hegadi

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Kunndra, C., Choudhary, A., Kaur, J., Mathur, P. (2024). VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_18

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  • DOI: https://doi.org/10.1007/978-3-031-53085-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53084-5

  • Online ISBN: 978-3-031-53085-2

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