IoT device identification based on network communication analysis using deep learning

Kotak, Jaidip; Elovici, Yuval

doi:10.1007/s12652-022-04415-6

IoT device identification based on network communication analysis using deep learning

Original Research
Published: 26 September 2022

Volume 14, pages 9113–9129, (2023)
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Abstract

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization’s network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization’s network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization’s network from non-white-listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the ’communication behavior’ of IoT devices is represented as small images which are generated from the device’s network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy.

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Notes

This is an extended and revised version of the paper (Kotak and Elovici 2019) that was presented at the CISIS 2020 conference and was published in its proceedings.

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Acknowledgements

This project was partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 830927.

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Authors and Affiliations

Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Jaidip Kotak & Yuval Elovici

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Jaidip Kotak
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Yuval Elovici
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Correspondence to Jaidip Kotak.

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Appendices

Appendix A: Performance evaluation results for the detection of a specific IoT device in the IoT device network communication

Actual IoT device/classified as	1	2	P	R	F1
1- Samsung SmartCam	813	0	1	1	1
2- Other IoT devices	0	5647	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Withings Aura smart sleep sensor	321	2	0.997	0.994	0.995
2- Other IoT devices	1	6136	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Insteon camera	364	1	1	0.997	0.999
2- Other IoT devices	0	6095	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Amazon Echo	305	2	1	0.993	0.997
2- Other IoT devices	0	6153	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Netatmo weather station	210	0	1	1	1
2- Other IoT devices	0	6250	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Netatmo welcome	239	3	1	0.988	0.994
2- Other IoT devices	0	6218	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Pix-Star photo frame	100	0	1	1	1
2- Other IoT devices	0	6360	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Belkin Wemo light switch	629	4	1	0.994	0.997
2- Other IoT devices	0	5827	0.999	1	1
	Weighted Avg		0.999	0.999	0.999

Actual IoT device/classified as	1	2	P	R	F1
1- Belkin Wemo motion sensor	3467	0	0.998	1	0.999
2- Other IoT devices	8	2985	1	0.997	0.999
	Weighted Avg		0.999	0.999	0.999

Appendix B: Performance evaluation results for the detection of a specific IoT device in the network communication

Actual IoT device/classified as	1	2	P	R	F1
1- Samsung SmartCam	813	0	1	1	1
2- Other devices	0	7873	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Withings Aura smart sleep sensor	322	1	1	0.997	0.998
2- Other devices	0	8363	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Insteon camera	364	1	1	0.997	0.999
2- Other devices	0	8321	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Amazon Echo	304	3	0.997	0.990	0.993
2- Other devices	1	8378	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Netatmo weather station	210	0	1	1	1
2- Other devices	0	8476	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Netatmo welcome	242	0	1	1	1
2- Other devices	0	8444	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Pix-Star photo frame	100	0	1	1	1
2- Other devices	0	8586	1	1	1
	Weighted Avg		1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Belkin Wemo light switch	631	2	1	0.997	0.998
2- Other devices	0	8053	1	1	1
		Weighted Avg	1	1	1

Actual IoT device/classified as	1	2	P	R	F1
1- Belkin Wemo motion sensor	3465	2	0.998	0.999	0.999
2- Other devices	7	5212	1	0.999	0.999
	Weighted Avg		0.999	0.999	0.999

Appendix C: Performance evaluation results for the detection of unauthorized IoT devices

Actual IoT device/classified as	1 (U)	2	3	4	5	6	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam (unknown)	802	4	0	0	0	0	0	0	7	0.93	0.986	0.958
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	0.988	1	0.994
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo	1	0	0	306	0	0	0	0	0	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	7	0	0	0	0	0	0	626	0	1	0.989	0.994
9- Belkin Wemo motion sensor	52	0	0	0	0	0	0	0	3415	0.998	0.985	0.991
								Weighted Avg		0.990	0.989	0.989

Actual IoT device/classified as	1	2 (U)	3	4	5	6	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	812	1	0	0	0	0	0	0	0	1	0.999	0.999
2- Withings Aura smart sleep sensor (unknown)	0	250	0	0	0	0	0	0	73	0.804	0.774	0.789
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo	0	1	0	306	0	0	0	0	0	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	7	0	0	0	0	0	626	0	1	0.989	0.994
9- Belkin Wemo motion sensor	0	52	0	0	0	0	0	0	3415	0.979	0.985	0.982
								Weighted Avg		0.979	0.979	0.979

Actual IoT device/classified as	1	2	3 (U)	4	5	6	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	811	0	1	0	0	0	0	0	1	1	0.998	0.999
2- Withings Aura smart sleep sensor	0	322	1	0	0	0	0	0	0	0.994	0.997	0.995
3- Insteon camera (unknown)	0	2	363	0	0	0	0	0	0	0.894	0.995	0.942
4- Amazon Echo	0	0	1	306	0	0	0	0	0	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo Welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	0	7	0	0	0	0	626	0	1	0.989	0.994
9- Belkin Wemo motion sensor	0	0	33	0	0	0	0	0	3434	1	0.990	0.995
								Weighted Avg		0.994	0.993	0.993

Actual IoT device/classified as	1	2	3	4 (U)	5	6	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	813	0	0	0	0	0	0	0	0	0.998	1	0.999
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	0.883	1	0.938
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo (unknown)	2	43	0	259	0	1	0	0	2	0.814	0.844	0.829
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	0.996	1	0.998
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	0	0	7	0	0	0	626	0	1	0.989	0.994
9- Belkin Wemo motion sensor	0	0	0	52	0	0	0	0	3415	0.999	0.985	0.992
								Weighted Avg		0.985	0.983	0.984

Actual IoT device/classified as	1	2	3	4	5 (U)	6	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	812	0	0	0	1	0	0	0	0	0.985	0.999	0.992
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	0.956	1	0.977
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo	0	0	0	306	1	0	0	0	0	1	0.997	0.998
5- Netatmo weather station (unknown)	12	15	0	0	144	0	0	37	2	0.966	0.686	0.802
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	0	0	0	1	0	0	626	6	0.944	0.989	0.966
9- Belkin Wemo motion sensor	0	0	0	0	2	0	0	0	3465	0.998	0.999	0.999
								Weighted Avg		0.988	0.988	0.987

Actual IoT device/classified as	1	2	3	4	5	6 (U)	7	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	813	0	0	0	0	0	0	0	0	1	1	1
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	1	1	1
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo	0	0	0	307	0	0	0	0	0	1	1	1
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome (unknown)	0	0	0	0	0	239	0	0	3	1	0.988	0.994
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	0	0	0	0	0	0	626	7	1	0.989	0.994
9- Belkin Wemo motion sensor	0	0	0	0	0	0	0	0	3467	0.997	1	0.999
								Weighted Avg		0.998	0.998	0.998

Actual IoT device/classified as	1	2	3	4	5	6	7 (U)	8	9	Precision	Recall	F1 score
1- Samsung SmartCam	813	0	0	0	0	0	0	0	0	1	1	1
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	1	1	1
3- Insteon camera	0	0	365	0	0	0	0	0	0	1	1	1
4- Amazon Echo	0	0	0	306	0	0	1	0	0	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame (unknown)	0	0	0	0	0	0	100	0	0	0.952	1	0.976
8- Belkin Wemo light switch	0	0	0	0	0	0	1	626	6	1	0.989	0.994
9- Belkin Wemo motion sensor	0	0	0	0	0	0	3	0	3464	0.998	0.999	0.999
								Weighted Avg		0.998	0.998	0.998

Actual IoT device/classified as	1	2	3	4	5	6	7	8 (U)	9	Precision	Recall	F1 score
1- Samsung SmartCam	812	0	0	0	0	0	0	1	0	1	0.999	0.999
2- Withings Aura smart sleep sensor	0	323	0	0	0	0	0	0	0	1	1	1
3- Insteon camera	0	0	364	0	0	0	0	1	0	1	0.997	0.999
4- Amazon Echo	0	0	0	306	0	0	0	1	0	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	210	0	0	0	0	1	1	1
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch (unknown)	0	0	0	0	0	0	0	622	11	0.995	0.983	0.989
9- Belkin Wemo motion sensor	0	0	0	0	0	0	0	0	3467	0.997	1	0.998
								Weighted Avg		0.998	0.998	0.998

Actual IoT device/classified as	1	2	3	4	5	6	7	8	9 (U)	Precision	Recall	F1 score
1- Samsung SmartCam	810	0	0	0	0	0	0	0	3	1	0.996	0.998
2- Withings Aura smart sleep sensor	0	322	0	0	0	0	0	0	1	1	0.997	0.998
3- Insteon camera	0	0	364	0	0	0	0	0	1	1	0.997	0.999
4- Amazon Echo	0	0	0	306	0	0	0	0	1	1	0.997	0.998
5- Netatmo weather station	0	0	0	0	209	0	0	0	1	1	0.995	0.998
6- Netatmo welcome	0	0	0	0	0	242	0	0	0	1	1	1
7- Pix-Star photo frame	0	0	0	0	0	0	100	0	0	1	1	1
8- Belkin Wemo light switch	0	0	0	0	0	0	0	633	0	0.920	1	0.958
9- Belkin Wemo motion sensor (unknown)	0	0	0	0	0	0	0	55	3412	0.998	0.984	0.991
								Weighted Avg		0.991	0.990	0.991

Appendix D: K-fold cross-validation results of different classifiers for the first four experiments

Experiment name	IoT device classifier	5-Fold cross-validation mean accuracy (%)	10-Fold cross-validation mean accuracy (%)
Experiment 1	IoT and non-IoT devices	99.57	99.66
Experiment 2	Samsung SmartCam	99.90	99.88
	Withings Aura smart sleep sensor	99.82	99.87
	Insteon camera	99.98	99.98
	Amazon Echo	99.88	99.91
	Netatmo weather station	99.99	99.99
	Netatmo Welcome	99.88	99.90
	Pix-Star photo frame	99.98	99.98
	Belkin Wemo light switch	99.83	99.83
	Belkin Wemo motion sensor	99.79	99.78
Experiment 3	Samsung SmartCam	99.92	99.93
	Withings Aura smart sleep sensor	99.87	99.89
	Insteon camera	99.98	99.98
	Amazon Echo	99.78	99.82
	Netatmo weather station	99.99	99.99
	Netatmo welcome	99.92	99.93
	Pix-Star photo frame	99.98	99.98
	Belkin Wemo light switch	99.88	99.88
	Belkin Wemo motion sensor	99.80	99.81
Experiment 4	Multiple IoT devices	99.43	99.63

* the k-fold cross-validation results for the fifth experiment are not listed, as it requires deriving a threshold from the validation set which is not feasible in k-fold cross-validation.

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Kotak, J., Elovici, Y. IoT device identification based on network communication analysis using deep learning. J Ambient Intell Human Comput 14, 9113–9129 (2023). https://doi.org/10.1007/s12652-022-04415-6

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Received: 06 January 2021
Accepted: 12 September 2022
Published: 26 September 2022
Issue Date: July 2023
DOI: https://doi.org/10.1007/s12652-022-04415-6

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IoT device identification based on network communication analysis using deep learning

Abstract

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Appendices

Appendix A: Performance evaluation results for the detection of a specific IoT device in the IoT device network communication

Appendix B: Performance evaluation results for the detection of a specific IoT device in the network communication

Appendix C: Performance evaluation results for the detection of unauthorized IoT devices

Appendix D: K-fold cross-validation results of different classifiers for the first four experiments

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About this article

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IoT device identification based on network communication analysis using deep learning

Abstract

Access this article

Similar content being viewed by others

A comprehensive survey of AI-enabled phishing attacks detection techniques

Deepfakes: current and future trends

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Notes

References

Acknowledgements

Author information

Authors and Affiliations

Corresponding author

Ethics declarations

Conflict of interest

Additional information

Publisher's Note

Appendices

Appendix A: Performance evaluation results for the detection of a specific IoT device in the IoT device network communication

Appendix B: Performance evaluation results for the detection of a specific IoT device in the network communication

Appendix C: Performance evaluation results for the detection of unauthorized IoT devices

Appendix D: K-fold cross-validation results of different classifiers for the first four experiments

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About this article

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