Ensuring IoT Privacy using Padding Strategies against Machine Learning Approaches

Abstract

The widespread usage of Internet of Things (IoT) devices is increasing by the recent advances in embedded systems, cloud computing, artificial intelligence, and wireless communications. Besides, a huge amount of data is transmitted between IoT devices over insecure networks. The transferred data can be sensitive and confidential. On the other hand, these transmitted data may not appear to be sensitive or confidential data. However, machine learning techniques are used on these non-confidential data (such as packet length) to obtain data such as the type of the IoT device. An observer can monitor traffic to infer sensitive data by using machine learning techniques to analyze the generated encrypted traffic. For this purpose, padding can be added to the packets to ensure traffic privacy. This paper presents privacy problems that are caused by the traffic generated during the communication of IoT devices. Also, security and privacy measures that should be taken against the related privacy problems are explained. For this purpose, the current studies are examined by considering the attacker and the defender models

Keywords

• Deep Learning
• Internet of Things
• Machine Learning
• Packet Length
• Padding
• Traffic Monitoring

References

1. Chegini, H., Naha, R. K., Mahanti, A., & Thulasiraman, P. (2021). Process automation in an IoT–fog–cloud ecosystem: A survey and taxonomy. IoT, 2(1), 92-118.
2. Benson, K. E., Wang, G., Venkatasubramanian, N., & Kim, Y. J. (2018, April). Ride: A resilient IoT data exchange middleware leveraging SDN and edge cloud resources. In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) (pp. 72-83). IEEE.
3. Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169-184
4. Saravanan, G., Parkhe, S. S., Thakar, C. M., Kulkarni, V. V., Mishra, H. G., & Gulothungan, G. (2022). Implementation of IoT in production and manufacturing: An Industry 4.0 approach. Materials Today: Proceedings, 51, 2427-2430.
5. Perez-Camacho, B. N., Rodriguez Gomez, G., Gonzalez-Calleros, J. M., & Martinez-López, F. J. (2022). Methodology for Designing an Electricity Demand System in the Context of IoT Household. Applied Sciences, 12(7), 3387.
6. Alam, T., & Benaida, M. (2018). CICS: cloud–internet communication security framework for the internet of smart devices. Tanweer Alam. Mohamed Benaida." CICS: Cloud–Internet Communication Security Framework for the Internet of Smart Devices.", International Journal of Interactive Mobile Technologies (iJIM), 12(6).
7. Hassan, W. H. (2019). Current research on Internet of Things (IoT) security: A survey. Computer networks, 148, 283-294.
8. Pinheiro, A. J., Bezerra, J. D. M., Burgardt, C. A., & Campelo, D. R. (2019). Identifying IoT devices and events based on packet length from encrypted traffic. Computer Communications, 144, 8-17.

9. Acar, A., Fereidooni, H., Abera, T., Sikder, A. K., Miettinen, M., Aksu, H., ... & Uluagac, S. (2020, July). Peek-a-boo: I see your smart home activities, even encrypted!. In Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (pp. 207-218).
10. Copos, B., Levitt, K., Bishop, M., & Rowe, J. (2016, May). Is anybody home? inferring activity from smart home network traffic. In 2016 IEEE Security and Privacy Workshops (SPW) (pp. 245-251). IEEE.
11. Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94.
12. Gupta, B. B., Chaudhary, P., Chang, X., & Nedjah, N. (2022). Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers. Computers & Electrical Engineering, 98, 107726.
13. Macedo, E. L., Delicato, F. C., de Moraes, L. F., & Fortino, G. (2022). Assigning Trust to Devices in the Context of Consumer IoT Applications. IEEE Consumer Electronics Magazine.
14. Kim, H., Lee, H., & Lim, H. (2020, February). Performance of packet analysis between observer and wireshark. In 2020 22nd International Conference on Advanced Communication Technology (ICACT) (pp. 268-271). IEEE.
15. Apthorpe, N., Reisman, D., & Feamster, N. (2017). A smart home is no castle: Privacy vulnerabilities of encrypted iot traffic. arXiv preprint arXiv:1705.06805.
16. Subahi, A., & Theodorakopoulos, G. (2019). Detecting IoT user behavior and sensitive information in encrypted IoT-app traffic. Sensors, 19(21), 4777.
17. Msadek, N., Soua, R., & Engel, T. (2019, April). Iot device fingerprinting: Machine learning based encrypted traffic analysis. In 2019 IEEE wireless communications and networking conference (WCNC) (pp. 1-8). IEEE.
18. Geng, Q., Ding, W., Guo, R., & Kumar, S. (2018). Privacy and utility tradeoff in approximate differential privacy. arXiv preprint arXiv:1810.00877.
19. Xu, L., Jiang, C., Chen, Y., Ren, Y., & Liu, K. R. (2015). Privacy or utility in data collection? A contract theoretic approach. IEEE Journal of Selected Topics in Signal Processing, 9(7), 1256-1269.
20. Frustaci, M., Pace, P., & Aloi, G. (2017, September). Securing the IoT world: Issues and perspectives. In 2017 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 246-251). IEEE.
21. Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2012, May). Peek-a-boo, i still see you: Why efficient traffic analysis countermeasures fail. In 2012 IEEE symposium on security and privacy (pp. 332-346). IEEE.
22. Pinheiro, A. J., Bezerra, J. M., & Campelo, D. R. (2018, June). Packet padding for improving privacy in consumer IoT. In 2018 IEEE Symposium on Computers and Communications (ISCC) (pp. 00925- 00929). IEEE.
23. Apthorpe, N., Reisman, D., Sundaresan, S., Narayanan, A., & Feamster, N. (2017). Spying on the smart home: Privacy attacks and defenses on encrypted iot traffic. arXiv preprint arXiv:1708.05044.
24. Apthorpe, N., Huang, D. Y., Reisman, D., Narayanan, A., & Feamster, N. (2018). Keeping the smart home private with smart (er) iot traffic shaping. arXiv preprint arXiv:1812.00955.
25. Pinheiro, A. J., de Araujo-Filho, P. F., Bezerra, J. D. M., & Campelo, D. R. (2020). Adaptive Packet Padding Approach for Smart Home Networks: A Tradeoff Between Privacy and Performance. IEEE Internet of Things Journal, 8(5), 3930-3938.
26. Engelberg, A., & Wool, A. (2021). Classification of Encrypted IoT Traffic Despite Padding and Shaping. arXiv preprint arXiv:2110.11188.
27. Vakili, M., Ghamsari, M., & Rezaei, M. (2020). Performance analysis and comparison of machine and deep learning algorithms for iot data classification. arXiv preprint arXiv:2001.09636.
28. Dong, S., Li, Z., Tang, D., Chen, J., Sun, M., & Zhang, K. (2020, October). Your smart home can't keep a secret: Towards automated fingerprinting of iot traffic. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (pp. 47-59).
29. Jovanov, E. (2019). Wearables meet IoT: Synergistic personal area networks (SPANs). Sensors, 19(19), 4295.
30. ElMoaqet, H., Ismael, I., Patzolt, F., & Ryalat, M. (2018, September). Design and integration of an IoT device for training purposes of industry 4.0. In Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control (pp. 1-5).
31. Irawan, Y., & Wahyuni, R. (2021, February). Electronic Equipment Control System for Households by using Android Based on IoT (Internet of Things). In Journal Of Physics: Conference Series (Vol. 1783, No. 1, p. 012094). IOP Publishing.
32. Sehrawat, D., & Gill, N. S. (2019, April). Smart sensors: Analysis of different types of IoT sensors. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 523-528). IEEE.
33. Adi, P. D. P., & Kitagawa, A. (2019). ZigBee Radio Frequency (RF) performance on Raspberry Pi 3 for Internet of Things (IoT) based blood pressure sensors monitoring. International Journal of Advanced Computer Science and Applications (IJACSA), 10(5), 18-27.
34. Basu, M. T., Karthik, R., Mahitha, J., & Reddy, V. L. (2018). IoT based forest fire detection system. International Journal of Engineering & Technology, 7(2.7), 124-126.
35. Palaiokrassas, G., Karlis, I., Litke, A., Charlaftis, V., & Varvarigou, T. (2017, July). An IoT architecture for personalized recommendations over big data oriented applications. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 475- 480). IEEE.
36. Jang, S. Y., Lee, Y., Shin, B., & Lee, D. (2018, October). Applicationaware IoT camera virtualization for video analytics edge computing. In 2018 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 132- 144). IEEE.
37. Aliero, M. S., Qureshi, K. N., Pasha, M. F., & Jeon, G. (2021). Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services. Environmental Technology & Innovation, 22, 101443.
38. Stolojescu-Crisan, C., Crisan, C., & Butunoi, B. P. (2021). An IoT-based smart home automation system. Sensors, 21(11), 3784.
39. Khalfalla, O. A., Ali, S. A., Altrjman, C., & Mubarak, A. S. (2022). Smart Home Appliance Control in the IoT Era. In International Conference on Forthcoming Networks and Sustainability in the IoT Era (pp. 392-397). Springer, Cham.
40. Inyangat, F. X. (2022). Smartphone agro-IoT application for smallholder farmers (Doctoral dissertation, Busitema University).
41. Alghuried, A. (2017). A model for anomalies detection in internet of things (IoT) using inverse weight clustering and decision tree. Masters’s Thesis, Dublin Institute of Technology, Dublin, Ireland.
42. Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20-28.
43. Aksoy, A., & Gunes, M. H. (2019, May). Automated iot device identification using network traffic. In ICC 2019-2019 IEEE International Conference on Communications (ICC) (pp. 1-7). IEEE.
44. Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017). An ensemble random forest algorithm for insurance big data analysis. Ieee access, 5, 16568- 16575.
45. Abdulkareem, N. M., & Abdulazeez, A. M. (2021). Machine learning classification based on Radom Forest Algorithm: A review. International Journal of Science and Business, 5(2), 128-142.
46. Dogru, N., & Subasi, A. (2018, February). Traffic accident detection using random forest classifier. In 2018 15th learning and technology conference (L&T) (pp. 40-45). IEEE.
47. Wang, H., Xu, P., & Zhao, J. (2021). Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities. Complexity, 2021.
48. Gawri, B., Kasturi, A., Neti, L. B. M., & Hota, C. (2022, January). An efficient approach to kNN algorithm for IoT devices. In 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS) (pp. 734-738). IEEE.
49. Hu, Y., Huber, A., Anumula, J., & Liu, S. C. (2018). Overcoming the vanishing gradient problem in plain recurrent networks. arXiv preprint arXiv:1801.06105
50. Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586.
51. Bai, L., Yao, L., Kanhere, S. S., Wang, X., & Yang, Z. (2018, October). Automatic device classification from network traffic streams of internet of things. In 2018 IEEE 43rd conference on local computer networks (LCN) (pp. 1-9). IEEE.
52. Datta, T., Apthorpe, N., & Feamster, N. (2018, August). A developerfriendly library for smart home iot privacy-preserving traffic obfuscation. In Proceedings of the 2018 Workshop on IoT Security and Privacy (pp. 43-48).