A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices

Aamo Iorliam

doi:10.35377/saucis...1354791

Research Article

Year 2023, , 218 - 225, 31.12.2023

Aamo Iorliam

https://doi.org/10.35377/saucis...1354791

Abstract

References

[1] A. Iorliam, A.T.S. Ho, A. Waller, and X. Zhao. "Using benford’s law divergence and neural networks for classification and source identification of biometric images." In Digital Forensics and Watermarking: 15th International Workshop, IWDW 2016, Beijing, China, September 17-19, 2016, Revised Selected Papers 15, pp. 88105. Springer International Publishing, 2017.
[2] J. Kotak, and E. Yuval. "IoT device identification using deep learning." 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020) 12. Springer International Publishing, 2021.
[3] C. Koball, P.R. Bhaskar, W. Yong, S. Tyler, and F. Connor "IoT Device Identification Using Unsupervised Machine Learning." Information 14.6, 2023
[4] A. Iorliam, A. Application of power laws to biometrics, forensics, and network traffic analysis. University of Surrey (United Kingdom), 2016.
[5] L. Bai, L. Yao, S. S. Kanhere, X. Wang, and Z. Yang. "Automatic device classification from network traffic streams of internet of things." 2018 IEEE 43rd conference on local computer networks (LCN). IEEE, 2018.
[6] I. Cvitić, D. Peraković, M. Periša, and B. Gupta. "Ensemble machine learning approach for classification of IoT devices in smart home." International Journal of Machine Learning and Cybernetics 12.11 (2021): 3179-3202.
[7] H. M. Zahid, Y. Saleem, F. Hayat, F. Z. Khan, R. Alroobaea, F. Almansour, M. Ahmad, and I. Ali. "A framework for identification and classification of iot devices for security analysis in heterogeneous network." Wireless Communications and Mobile Computing 2022 (2022).
[8] A. R. Zarzoor, N.A.S. Al-Jamali, and I.R.K. Al-Saedi. "Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach." Mathematical Modelling of Engineering Problems 10.2 (2023).
[9] Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici. "N-baiot—networkbased detection of iot botnet attacks using deep autoencoders." IEEE Pervasive Computing 17.3 (2018): 12-22.
[10] A. Iorliam, A., S. Tirunagari, A.T. Ho, S. Li, A. Waller, and N. Poh."" Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law." arXiv preprint arXiv:1609.04214 (2016).
[11] K. Sethi, E. Sai Rupesh, R. Kumar, P. Bera, and Y. Venu Madhav "A context-aware robust intrusion detection system: a reinforcement learning-based approach." International Journal of Information Security 19 (2020): 657678.
[12] S. Albawi, T.A.M. Mohammed, and S. Al-Zawi. "Understanding of a convolutional neural network." 2017 international conference on engineering and technology (ICET). IEEE, 2017.

A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices

Year 2023, , 218 - 225, 31.12.2023

Aamo Iorliam

https://doi.org/10.35377/saucis...1354791

Abstract

The inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source where the IoT data is generated and also separate an IoT device from the other using on the data they generate. This paper proposes a novel additive IoT features with the CNN system for the purpose of IoT source identification and classification. Experimental results shows that indeed the proposed method is very effective achieving an overall classification and source identification accuracy of 99.67 %. This result has a practical application to forensics purposes due to the fact that accurately identifying and classifying the source of an IoT device via the generated data can link organisations/persons to the activities they perform over the network. As such ensuring accountability and responsibility by IoT device users.

Keywords

Internet of Things (IoT), Additive IoT Features, Inter-class classification, Source identification

References

[1] A. Iorliam, A.T.S. Ho, A. Waller, and X. Zhao. "Using benford’s law divergence and neural networks for classification and source identification of biometric images." In Digital Forensics and Watermarking: 15th International Workshop, IWDW 2016, Beijing, China, September 17-19, 2016, Revised Selected Papers 15, pp. 88105. Springer International Publishing, 2017.
[2] J. Kotak, and E. Yuval. "IoT device identification using deep learning." 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020) 12. Springer International Publishing, 2021.
[3] C. Koball, P.R. Bhaskar, W. Yong, S. Tyler, and F. Connor "IoT Device Identification Using Unsupervised Machine Learning." Information 14.6, 2023
[4] A. Iorliam, A. Application of power laws to biometrics, forensics, and network traffic analysis. University of Surrey (United Kingdom), 2016.
[5] L. Bai, L. Yao, S. S. Kanhere, X. Wang, and Z. Yang. "Automatic device classification from network traffic streams of internet of things." 2018 IEEE 43rd conference on local computer networks (LCN). IEEE, 2018.
[6] I. Cvitić, D. Peraković, M. Periša, and B. Gupta. "Ensemble machine learning approach for classification of IoT devices in smart home." International Journal of Machine Learning and Cybernetics 12.11 (2021): 3179-3202.
[7] H. M. Zahid, Y. Saleem, F. Hayat, F. Z. Khan, R. Alroobaea, F. Almansour, M. Ahmad, and I. Ali. "A framework for identification and classification of iot devices for security analysis in heterogeneous network." Wireless Communications and Mobile Computing 2022 (2022).
[8] A. R. Zarzoor, N.A.S. Al-Jamali, and I.R.K. Al-Saedi. "Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach." Mathematical Modelling of Engineering Problems 10.2 (2023).
[9] Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici. "N-baiot—networkbased detection of iot botnet attacks using deep autoencoders." IEEE Pervasive Computing 17.3 (2018): 12-22.
[10] A. Iorliam, A., S. Tirunagari, A.T. Ho, S. Li, A. Waller, and N. Poh."" Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law." arXiv preprint arXiv:1609.04214 (2016).
[11] K. Sethi, E. Sai Rupesh, R. Kumar, P. Bera, and Y. Venu Madhav "A context-aware robust intrusion detection system: a reinforcement learning-based approach." International Journal of Information Security 19 (2020): 657678.
[12] S. Albawi, T.A.M. Mohammed, and S. Al-Zawi. "Understanding of a convolutional neural network." 2017 international conference on engineering and technology (ICET). IEEE, 2017.

There are 12 citations in total.

Details

Primary Language	English
Subjects	Computer Software
Journal Section	Articles
Authors	Aamo Iorliam 0000-0001-8238-9686
Early Pub Date	December 27, 2023
Publication Date	December 31, 2023
Submission Date	September 4, 2023
Acceptance Date	November 15, 2023
Published in Issue	Year 2023

Cite

IEEE	A. Iorliam, “A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices”, SAUCIS, vol. 6, no. 3, pp. 218–225, 2023, doi: 10.35377/saucis...1354791.