Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review

Authors

DOI:

https://doi.org/10.4108/eetsc.v7i1.2067

Keywords:

IOT, DL, ML, Challenges, IoT Applications

Abstract

The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications.

Downloads

Download data is not yet available.

References

M. Swan, "Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0," Journal of Sensor and Actuator networks, vol. 1, no. 3, pp. 217-253, 2012.

C. Cai, M. Hu, D. Cao, X. Ma, Q. Li, and J. Liu, "Self-deployable indoor localization with acoustic-enabled IoT devices exploiting participatory sensing," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5297-5311, 2019.

C. Wang, H. Lin, and H. Jiang, "CANS: Towards congestion-adaptive and small stretch emergency navigation with wireless sensor networks," IEEE Transactions on Mobile Computing, vol. 15, no. 5, pp. 1077-1089, 2015.

S. Lateef, M. Rizwan, and M. A. Hassan, "Security Threats in Flying Ad Hoc Network (FANET)," Computational Intelligence for Unmanned Aerial Vehicles Communication Networks, pp. 73-96, 2022.

M. Hu et al., "On the joint design of routing and scheduling for vehicle-assisted multi-UAV inspection," Future Generation Computer Systems, vol. 94, pp. 214-223, 2019.

M. Chen, F. Herrera, and K. Hwang, "Cognitive computing: architecture, technologies and intelligent applications," Ieee Access, vol. 6, pp. 19774-19783, 2018.

A. Hussain, M. Imad, A. Khan, and B. Ullah, "Multi-class Classification for the Identification of COVID-19 in X-Ray Images Using Customized Efficient Neural Network," in AI and IoT for Sustainable Development in Emerging Countries: Springer, 2022, pp. 473-486.

S. P. RM et al., "Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything," Journal of parallel and distributed computing, vol. 142, pp. 16-26, 2020.

H. Zeyu, X. Geming, W. Zhaohang, and Y. Sen, "Survey on edge computing security," in 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2020: IEEE, pp. 96-105.

F. E. F. Samann, S. R. Zeebaree, and S. Askar, "IoT provisioning QoS based on cloud and fog computing," Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 29-40, 2021.

S. Shehzadi, M. A. Hassan, M. Rizwan, N. Kryvinska, and K. Vincent, "Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques," Computational Intelligence and Neuroscience, vol. 2022, 2022.K. Ali and S. Askar, "Security Issues and Vulnerability of IoT Devices," International Journal of Science and Business, vol. 5, no. 3, pp. 101-115, 2021.

O. Uviase and G. Kotonya, "IoT architectural framework: connection and integration framework for IoT systems," arXiv preprint arXiv:1803.04780, 2018.

S. I. Ullah, A. W. Ullah, A. Salam, M. Imad, and F. Ullah, "Performance Analysis of POX and RYU Based on Dijkstra’s Algorithm for Software Defined Networking," in European, Asian, Middle Eastern, North African Conference on Management & Information Systems, 2021: Springer, pp. 24-35.

F. S. Fizi and S. Askar, "A novel load balancing algorithm for software defined network based datacenters," in 2016 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), 2016: IEEE, pp. 1-6.

S. K. Askar, "Adaptive load balancing scheme for data center networks using software defined network," Science Journal of University of Zakho, vol. 4, no. 2, pp. 275-286, 2016.

Ahmad, S. and Hassan, M., 2022. Secure Communication Routing in FANETs: A Survey. Studies in Computational Intelligence, pp.97-110.

G. A. Qadir and S. Askar, "Software Defined Network Based VANET," International Journal of Science and Business, vol. 5, no. 3, pp. 83-91, 2021.

S. Askar, G. Zervas, D. K. Hunter, and D. Simeonidou, "Adaptive classified cloning and aggregation technique for delay and loss sensitive applications in OBS networks," in 2011 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference, 2011: IEEE, pp. 1-3.

M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, "Machine learning for Internet of Things data analysis: A survey," Digital Communications and Networks, vol. 4, no. 3, pp. 161-175, 2018.

H. U. Rehman, M. Asif, and M. Ahmad, "Future applications and research challenges of IOT," in 2017 international conference on information and communication technologies (ICICT), 2017: IEEE, pp. 68-74.

A. Salam, F. Ullah, M. Imad, and M. A. Hassan, "Diagnosing of Dermoscopic Images using Machine Learning approaches for Melanoma Detection," in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020: IEEE, pp. 1-5.

I. Kök, M. U. Şimşek, and S. Özdemir, "A deep learning model for air quality prediction in smart cities," in 2017 IEEE International Conference on Big Data (Big Data), 2017: IEEE, pp. 1983-1990.

F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, "A review of machine learning and IoT in smart transportation," Future Internet, vol. 11, no. 4, p. 94, 2019.

M. A. Hassan, M. Imad, T. Hassan, F. Ullah, and S. Ahmad, "Impact of Routing Techniques and Mobility Models on Flying Ad Hoc Networks," in Computational Intelligence for Unmanned Aerial Vehicles Communication Networks: Springer, 2022, pp. 111-129.

G. Alpár et al., "New directions in IoT privacy using attribute-based authentication," in Proceedings of the ACM International Conference on Computing Frontiers, 2016, pp. 461-466.

E. Yadav and E. Ankur, "A survey of growth and opportunity of Internet of Things (IoT) in Global Scenario," International Journal of Innovative Research in Computer and Communication Engineering, vol. 4, pp. 20664-20671, 2016.

J. B. Bernabe, J. L. Hernandez-Ramos, and A. F. S. Gomez, "Holistic Privacy-Preserving Identity Management System for the Internet of Things," Mobile Information Systems, 2017.

M. Imad, F. Ullah, and M. A. Hassan, "Pakistani Currency Recognition to Assist Blind Person Based on Convolutional Neural Network," Journal of Computer Science and Technology Studies, vol. 2, no. 2, pp. 12-19, 2020.

M. Rizwan et al., "Risk monitoring strategy for confidentiality of healthcare information," Computers and Electrical Engineering, vol. 100, p. 107833, 2022.

M. A. Hassan, S. I. Ullah, A. Salam, A. W. Ullah, M. Imad, and F. Ullah, "Energy efficient hierarchical based fish eye state routing protocol for flying ad-hoc networks," Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 1, pp. 465-471, 2021.

D. Bandyopadhyay and J. Sen, "Internet of things: Applications and challenges in technology and standardization," Wireless personal communications, vol. 58, no. 1, pp. 49-69, 2011.

M. A. Hassan, A. R. Javed, T. Hassan, S. S. Band, R. Sitharthan, and M. Rizwan, "Reinforcing Communication on the Internet of Aerial Vehicles," IEEE Transactions on Green Communications and Networking, 2022.

S. I. Ullah, A. Salam, W. Ullah, and M. Imad, "COVID-19 lung image classification based on logistic regression and support vector machine," in European, Asian, Middle Eastern, North African Conference on Management & Information Systems, 2021: Springer, pp. 13-23.

H. R. Abdulqadir et al., "A study of moving from cloud computing to fog computing," Qubahan Academic Journal, vol. 1, no. 2, pp. 60-70, 2021.

T. Wang, M. Z. A. Bhuiyan, G. Wang, L. Qi, J. Wu, and T. Hayajneh, "Preserving balance between privacy and data integrity in edge-assisted Internet of Things," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2679-2689, 2019.

B. Sharma, L. Sharma, and C. Lal, "Anomaly detection techniques using deep learning in IoT: a survey," in 2019 International conference on computational intelligence and knowledge economy (ICCIKE), 2019: IEEE, pp. 146-149.

M. Imad, A. Hussain, M. A. Hassan, Z. Butt, and N. U. Sahar, "IoT Based Machine Learning and Deep Learning Platform for COVID-19 Prevention and Control: A Systematic Review," AI and IoT for Sustainable Development in Emerging Countries, pp. 523-536, 2022.

Lateef, S., Rizwan, M. and Hassan, M., 2022. Security Threats in Flying Ad Hoc Network (FANET). Studies in Computational Intelligence, pp.73-96.

H.-T. Pai, S.-H. Wang, T.-S. Chang, and J.-X. Wu, "Challenge of anomaly detection in IoT analytics," in 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), 2020: IEEE, pp. 1-2.

M. Imad, S. I. Ullah, A. Salam, W. U. Khan, F. Ullah, and M. A. Hassan, "Automatic Detection of Bullet in Human Body Based on X-Ray Images Using Machine Learning Techniques," International Journal of Computer Science and Information Security (IJCSIS), vol. 18, no. 6, 2020.

M.Imad, N. Khan, F. Ullah, M. A. Hassan, and A. Hussain, "COVID-19 classification based on Chest X-Ray images using machine learning techniques," Journal of Computer Science and Technology Studies, vol. 2, no. 2, pp. 01-11, 2020.

Hassan, M., Ullah, S., Khan, I., Hussain Shah, S., Salam, A. and Ullah Khan, A., 2020. Unmanned Aerial Vehicles Routing Formation Using Fisheye State Routing for Flying Ad-hoc Networks. The 4th International Conference on Future Networks and Distributed Systems (ICFNDS).

M. Imad, M. Abul Hassan, S. Hussain Bangash and Naimullah, "A Comparative Analysis of Intrusion Detection in IoT Network Using Machine Learning", Studies in Big Data, pp. 149-163, 2022. Available: 10.1007/978-3-031-05752-6_10.

M. Hassan, S. Ali, M. Imad and S. Bibi, "New Advancements in Cybersecurity: A Comprehensive Survey", Studies in Big Data, pp. 3-17, 2022. Available: 10.1007/978-3-031-05752-6_1.

Downloads

Published

17-01-2023

How to Cite

[1]
A. Shoukat, “Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review”, EAI Endorsed Trans Smart Cities, vol. 7, no. 1, p. e1, Jan. 2023.