Managing security in IoT by applying the deep neural network-based security framework
Keywords:Internet of things, security, artificial intelligence, fog computing, wireless sensors, security threats
Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the phase of an assault life span based on the concept of the cyberspace killing chain. It outlines each long-term security in the proposed framework and proves its effectiveness in practical applications across diverse threats. In addition, each risk in the borders layer is dealt with by integrating artificial intelligence (AI) safety modules into a separate layer of AI-SM-IoT delivered by services. It contrasted the system framework with the previous designs. It described the architectural freedom from the base areas of the project and its relatively low latency, which provides safety as a service rather than an embedded network edge on the internet-of-things design. It assessed the proposed design based on the administration score of the IoT platform, throughput, security, and working time
- Oniani, S., Marques, G., Barnovi, S., Pires, I. M., Bhoi, A. K. (2020). Artificial Intelligence for Internet of Things and Enhanced Medical Systems. Studies in Computational Intelligence, 43–59. doi: https://doi.org/10.1007/978-981-15-5495-7_3
- Su, J., Chu, X., Kadry, S., S, R. (2020). Internet-of-Things-Assisted Smart System 4.0 Framework Using Simulated Routing Procedures. Sustainability, 12 (15), 6119. doi: https://doi.org/10.3390/su12156119
- El-Latif, A. A. A., Abd-El-Atty, B., Mazurczyk, W., Fung, C., Venegas-Andraca, S. E. (2020). Secure Data Encryption Based on Quantum Walks for 5G Internet of Things Scenario. IEEE Transactions on Network and Service Management, 17 (1), 118–131. doi: https://doi.org/10.1109/tnsm.2020.2969863
- Chakraborty, N., Li, J.-Q., Mondal, S., Luo, C., Wang, H., Alazab, M. et al. (2021). On Designing a Lesser Obtrusive Authentication Protocol to Prevent Machine-Learning-Based Threats in Internet of Things. IEEE Internet of Things Journal, 8 (5), 3255–3267. doi: https://doi.org/10.1109/jiot.2020.3025274
- Manogaran, G., Mumtaz, S., Mavromoustakis, C. X., Pallis, E., Mastorakis, G. (2021). Artificial Intelligence and Blockchain-Assisted Offloading Approach for Data Availability Maximization in Edge Nodes. IEEE Transactions on Vehicular Technology, 70 (3), 2404–2412. doi: https://doi.org/10.1109/tvt.2021.3058689
- Zheng, W., Muthu, B., Kadry, S. N. (2021). Research on the design of analytical communication and information model for teaching resources with cloud‐sharing platform. Computer Applications in Engineering Education, 29 (2), 359–369. doi: https://doi.org/10.1002/cae.22375
- Wang, W., Jackson Samuel, R. D., Hsu, C.-H. (2020). Prediction architecture of deep learning assisted short long term neural network for advanced traffic critical prediction system using remote sensing data. European Journal of Remote Sensing, 54 (sup2), 65–76. doi: https://doi.org/10.1080/22797254.2020.1755998
- Rauf, H. T., Gao, J., Almadhor, A., Arif, M., Nafis, M. T. (2021). Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM. Soft Computing, 25 (20), 12989–12999. doi: https://doi.org/10.1007/s00500-021-06075-8
- Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., Jaber, M. M. (2018). RETRACTED ARTICLE: Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks. Journal of Medical Systems, 42 (10). doi: https://doi.org/10.1007/s10916-018-1045-z
- Amudha, G., Narayanasamy, P. (2018). Distributed Location and Trust Based Replica Detection in Wireless Sensor Networks. Wireless Personal Communications, 102 (4), 3303–3321. doi: https://doi.org/10.1007/s11277-018-5369-2
- Nguyen, T. N., Le, V. V., Chu, S.-I., Liu, B.-H., Hsu, Y.-C. (2021). Secure Localization Algorithms Against Localization Attacks in Wireless Sensor Networks. Wireless Personal Communications, 127 (1), 767–792. doi: https://doi.org/10.1007/s11277-021-08404-4
- Malarvizhi Kumar, P., Choong Seon, H. (2021). RETRACTED ARTICLE: Internet of Things-Based Digital Video Intrusion for Intelligent Monitoring Approach. Arabian Journal for Science and Engineering. doi: https://doi.org/10.1007/s13369-021-05902-2
- Manickam, A., Jiang, J., Zhou, Y., Sagar, A., Soundrapandiyan, R., Dinesh Jackson Samuel, R. (2021). Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures. Measurement, 184, 109953. doi: https://doi.org/10.1016/j.measurement.2021.109953
- Sheron, P. S. F., Sridhar, K. P., Baskar, S., Shakeel, P. M. (2019). A decentralized scalable security framework for end‐to‐end authentication of future IoT communication. Transactions on Emerging Telecommunications Technologies, 31 (12). doi: https://doi.org/10.1002/ett.3815
- Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 127 (1), 85–105. doi: https://doi.org/10.1007/s11277-021-08094-y
- Gheisari, M., Najafabadi, H. E., Alzubi, J. A., Gao, J., Wang, G., Abbasi, A. A., Castiglione, A. (2021). OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city. Future Generation Computer Systems, 123, 1–13. doi: https://doi.org/10.1016/j.future.2021.01.028
- Nguyen, T. N., Liu, B.-H., Nguyen, N. P., Dumba, B., Chou, J.-T. (2021). Smart Grid Vulnerability and Defense Analysis Under Cascading Failure Attacks. IEEE Transactions on Power Delivery, 36 (4), 2264–2273. doi: https://doi.org/10.1109/tpwrd.2021.3061358
- Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63, 102364. doi: https://doi.org/10.1016/j.scs.2020.102364
- Javaid, N., Sher, A., Nasir, H., Guizani, N. (2018). Intelligence in IoT-Based 5G Networks: Opportunities and Challenges. IEEE Communications Magazine, 56 (10), 94–100. doi: https://doi.org/10.1109/mcom.2018.1800036
- Mao, B., Kawamoto, Y., Kato, N. (2020). AI-Based Joint Optimization of QoS and Security for 6G Energy Harvesting Internet of Things. IEEE Internet of Things Journal, 7 (8), 7032–7042. doi: https://doi.org/10.1109/jiot.2020.2982417
- Mendhurwar, S., Mishra, R. (2019). Integration of social and IoT technologies: architectural framework for digital transformation and cyber security challenges. Enterprise Information Systems, 15 (4), 565–584. doi: https://doi.org/10.1080/17517575.2019.1600041
- Mukherjee, A., Goswami, P., Yang, L., Sah Tyagi, S. K., Samal, U. C., Mohapatra, S. K. (2020). Deep neural network-based clustering technique for secure IIoT. Neural Computing and Applications, 32 (20), 16109–16117. doi: https://doi.org/10.1007/s00521-020-04763-4
- Vimal, S., Khari, M., Crespo, R. G., Kalaivani, L., Dey, N., Kaliappan, M. (2020). Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks. Computer Communications, 154, 481–490. doi: https://doi.org/10.1016/j.comcom.2020.03.004
- Alqaralleh, B. A. Y., Vaiyapuri, T., Parvathy, V. S., Gupta, D., Khanna, A., Shankar, K. (2021). Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things Environment. Personal and Ubiquitous Computing. doi: https://doi.org/10.1007/s00779-021-01543-2
- Ahmed Jamal, A., Mustafa Majid, A.-A., Konev, A., Kosachenko, T., Shelupanov, A. (2021). A review on security analysis of cyber physical systems using Machine learning. Materials Today: Proceedings. doi: https://doi.org/10.1016/j.matpr.2021.06.320
- Cui, Z., Xue, F., Zhang, S., Cai, X., Cao, Y., Zhang, W., Chen, J. (2020). A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN. IEEE Transactions on Services Computing, 1–1. doi: https://doi.org/10.1109/tsc.2020.2964537
- Aldhaheri, S., Alghazzawi, D., Cheng, L., Barnawi, A., Alzahrani, B. A. (2020). Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications, 157, 102537. doi: https://doi.org/10.1016/j.jnca.2020.102537
- Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F. (2018). Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system. Computing, 101 (11), 1661–1685. doi: https://doi.org/10.1007/s00607-018-0680-z
- Zaidan, A. A., Zaidan, B. B. (2018). A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review, 53 (1), 141–165. doi: https://doi.org/10.1007/s10462-018-9648-9
- Kumar, P., Kumar, R., Gupta, G. P., Tripathi, R. (2020). A Distributed framework for detecting DDoS attacks in smart contract‐based Blockchain‐IoT Systems by leveraging Fog computing. Transactions on Emerging Telecommunications Technologies, 32 (6). doi: https://doi.org/10.1002/ett.4112
- Sultana, T., Wahid, K. A. (2019). IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management. IEEE Access, 7, 134881–134894. doi: https://doi.org/10.1109/access.2019.2941978
- Li, D., Deng, L., Liu, W., Su, Q. (2020). Improving communication precision of IoT through behavior-based learning in smart city environment. Future Generation Computer Systems, 108, 512–520. doi: https://doi.org/10.1016/j.future.2020.02.053
- Edge-IIoTset Cyber Security Dataset of IoT & IIoT. Available at: https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot
How to Cite
Copyright (c) 2022 Nabeel Mahdy Haddad, Hayder Sabah Salih, Ban Salman Shukur, Sura Khalil Abd, Mohammed Hasan Ali, Rami Qais Malik
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with PC TECHNOLOGY CENTER, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher PC TECHNOLOGY CENTER does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.