Abstract
The Internet has become an inextricable element of human life, and the number of Internet-connected gadgets is rapidly growing. Internet of Things (IoT) gadgets, in particular, has become an integral component of modern life. IoT network participants are generally resource constrained, rendering them vulnerable to cyber-threats. Classic cryptographic techniques have been extensively used to deal with the safety and confidentiality problems in IoT systems in this regard. Due to the exclusive qualities of IoT nodes, available results are unable to cover the complete defense spectrum of IoT networks. However, some difficulties are becoming more prevalent, and their remedies are unclear. The IoT is posing an increasing number of issues in terms of technological security. The Internet of Things, on the other hand, has been shown to be prone to security breaches. To address security concerns, it is critical to establish effective solutions through the progress of the latest technologies or the integration of obtainable technology. Deep learning, a division of machine learning, has previously demonstrated potential for finding security vulnerabilities. IoT devices also generate a lot of data with a lot of variety and veracity. As a result, by using big data technologies, it is possible to achieve enhanced speed and data management. In this research, we examine the safety necessities, assault vectors, and existing safety resolutions for IoT systems and offer a ground-breaking deep learning strategy for IoT security.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mohan, N., & Kangasharju, J. (2016). Edge-fog cloud: A distributed cloud for internet of things computations. In Proceedings of cloudification of the internet of things (CIoT) (pp. 1–6). https://doi.org/10.1109/CIOT.2016.7872914
Habeeb, R. A. A., Nasaruddin, F., Gani, A., Hashem, I. A. T., Ahmed, E., & Imran, M. (2019). Real-time big data processing for anomaly detection: A survey. International Journal of Information Management, 45, 289–307.
Davis, G., & Davis, G. (2018). Trending: IoT malware attacks of 2018. https://securingtomorrow.mcafee.com/consumer/mobile-and-iot-security/top-trending-iot-malware-attacks-of-2018. Accessed on May 10, 2019.
Wong, W. G. (2015). Developers discuss IoT security and platforms trends. https://www.electronicdesign.com/embedded/developers-discuss-iot-security-and-platforms-trends. Accessed on May 1, 2019.
New trends in the world of iot threats. https://securelist.com/new-trends-in-the-world-of-iot-threats/87991/. Accessed on May 10, 2019
Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and good practices. In: 2013 6th International Conference on Contemporary Computing (IC3), IEEE. https://doi.org/10.1109/ic3.2013.6612229
Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74–76. https://doi.org/10.1109/msp.2013.138
McDermott, C. D., Majdani, F., & Petrovski, A. V. (2018). Botnet detection in the internet of things using deep learning approaches. In: 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE
Aly, M., Khomh, F., Haoues, M., Quintero, A., & Yacout, S. (2019). Enforcing security in internet of things frameworks: A systematic literature review. Internet of Things 100050.
Pan, J., & Yang, Z. (2018). Cybersecurity challenges and opportunities in the new edge computing+ IoT world. In: Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization (pp. 29–32). ACM
Reddy Madhavi, K., Vijaya Sambhavi, Y., Sudhakara, M., & Srujan Raju, K. (2021). Covid-19 isolation monitoring System, Springer series—Lecture Notes on Data Engineering and Communication Technology. https://doi.org/10.1007/978-981-16-0081-4_60
Rizwan, P., Suresh, K., & Babu, M. R. (2016). Real-time smart traffic management system for smart cities by using Internet of Things and big data. In 2016 International Conference on Emerging Technological Trends (ICETT). IEEE.
Sekaran, R., Goddumarri, S. N., Kallam, S., Patan, R., Ramachandran, M., Al-Turjman, F. (2021). 5G integrated spectrum selection and spectrum access using ai-based framework for iot based sensor networks, Computer Networks, 186.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Venkatesh, M., Srinu, M., Gudivada, V.K., Dash, B.B., Satpathy, R. (2023). An Efficient IoT Security Solution Using Deep Learning Mechanisms. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-19-4162-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4161-0
Online ISBN: 978-981-19-4162-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)