Sharma, A., Singh, P. K. & Kumar, Y. An integrated fire detection system using IoT and image processing technique for smart cities. Sustain. Cities Soc. 61, e4826 (2020).
Google Scholar
Sinan, K. SDG-11: Sustainable Cities and Communities. Emerging Technologies, Sustainable Development Goals Series 1st edn. (Springer, 2020).
Hussain, F., Hussain, R., Hassan, S. A. & Hossain, E. Machine learning in IoT security: Current solutions and future challenges. IEEE Commun. Surv. Tutor. 22(3), 1686–1721 (2020).
Google Scholar
Bharati, S., Mondal, M. R. H., Podder, P. & Prasath, V. B. Federated learning: Applications, challenges and future directions. Int. J. Hybrid Intell. Syst. 18(1–2), 19–35 (2022).
Shafiq, M., Tian, Z., Bashir, A. K., Du, X. & Guizani, M. Corrauc: A malicious BOT-IOT traffic detection method in IoT network using machine learning techniques. IEEE Internet Things J. 8(5), 3242–3254 (2020).
Google Scholar
Omolara, A. E. et al. The Internet of Things security: A survey encompassing unexplored areas and new insights. Comput. Secur. 112, 102494 (2022).
Google Scholar
Bharati, S., Podder, P., Mondal, M. R. H. & Paul, P. K. Applications and challenges of cloud integrated IoMT. In Cognitive Internet of Medical Things for Smart Healthcare 1st edn (eds Hassanien, A. E. et al.) 67–85 (Springer, 2021).
Google Scholar
Özalp, A. N. et al. Layer-based examination of cyber-attacks in IoT. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (IEEE, 2022).
Altunay, H. C. & Albayrak, Z. A hybrid CNN+ LSTM—Based intrusion detection system for industrial IoT networks. Eng. Sci. Technol. Int. J. 38, 101322 (2023).
Abbas, Y., Ali, D., Gautam, S., Hadis, K. & Reza, M. P. Hybrid privacy preserving federated learning against irregular users in next-generation Internet of Things. J. Syst. Archit. 148, 103088 (2024).
Google Scholar
Abbas, Y., Ali, D. & Gautam, S. AP2FL: Auditable privacy-preserving federated learning framework for electronics in healthcare. IEEE Trans. Consumer Electron. 99, 1 (2023).
Danyal, N., Abbas, Y., Ali, D. & Gautam, S. Federated quantum-based privacy-preserving threat detection model for consumer Internet of Things. IEEE Trans. Consumer Electron. (2024).
Google Scholar
Sanaz, N., Behrouz, Z., Abbas, Y. & Ali, D. Steeleye: An application-layer attack detection and attribution model in industrial control systems using semi-deep learning. In 2021 18th International Conference on Privacy, Security and Trust (PST), IEEE Xplore (2021).
Abbas, Y., Ali, D., Reza, M. P., Gautam, S. & Hadis, K. Secure intelligent fuzzy blockchain framework: Effective threat detection in IoT networks. Comput. Ind. 144, 103801 (2023).
Google Scholar
Gopi, K. J., Abbas, Y., Reza, M. P. & Seyedamin, P. Exploring privacy measurement in federated learning. J. Supercomput. 1, 43 (2023).
Otoum, Y. & Nayak, A. On securing IoT from deep learning perspective. In Proc. 2020 IEEE Symposium on Computers and Communications (ISCC) 1–7 (2020).
Butun, I., Sterberg, P. O. & Song, H. Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures. IEEE Commun. Surv. Tutor. 22(1), 616–644 (2020).
Google Scholar
Tahsien, S. M., Karimipour, H. & Spachos, P. Machine learning based solutions for security of Internet of Things (IoT): A survey. J. Netw. Comput. Appl. 161, 102630 (2020).
Google Scholar
Abiodun, O. I., Abiodun, E. O., Alawida, M., Alkhawaldeh, R. S. & Arshad, H. A review on the security of the Internet of Things: Challenges and solutions. Wirel. Person. Commun. 119(3), 2603–2637 (2021).
Google Scholar
Podder, P., Mondal, M. R. H., Bharati, S. & Paul, P. K. Review on the security threats of Internet of Things. Int. J. Comput. Appl. 176(41), 37–45 (2020).
Hamad, Z. J. & Askar, S. Machine learning powered IoT for smart applications. Int. J. Sci. Bus. 5(3), 92–100 (2021).
Xu, H. et al. A combination strategy of feature selection based on an integrated optimization algorithm and weighted K-nearest neighbor to improve the performance of network intrusion detection. Electronics 9(8), 1206 (2020).
Google Scholar
Bharati, S. & Mondal, M. R. H. Computational intelligence for managing pandemics. In 12 Applications and Challenges of AI-Driven IoHT for Combating Pandemics: A Review (eds Bharati, S. & Mondal, M. R. H.) 213–230 (De Gruyter, 2021).
Robel, M. R. A., Bharati, S., Podder, P. & Mondal, M. R. H. IoT driven healthcare monitoring system. In Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications (eds Gupta, D. & Khamparia, A.) 161–176 (Wiley, 2020).
Google Scholar
Podder, P., Mondal, M. R. H. & Kamruzzaman, J. Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern. In Applications of Computational Intelligence in Multi-Disciplinary Research 1st edn (eds Elngar, A. A. et al.) (Elsevier, 2022).
Chandavarkar, B. R. Hardcoded credentials and insecure data transfer in IoT: National and international status. In Proc. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 1–7 (2020).
Ferrara, P., Mandal, A. K., Cortesi, A. & Spoto, F. Static analysis for discovering IoT vulnerabilities. Int. J. Softw. Tools Technol. Transf. 23(1), 71–88 (2021).
Google Scholar
Yu, Y., Guo, L., Liu, S., Zheng, J. & Wang, H. Privacy protection scheme based on CP-ABE in crowdsourcing-IoT for Smart Ocean. IEEE Internet Things J. 7(10), 10061–10071 (2020).
Google Scholar
Xiong, J. et al. A personalized privacy protection framework for mobile crowdsensing in IIoT. IEEE Trans. Ind. Inform. 16(6), 4231–4241 (2020).
Google Scholar
Jiang, X., Lora, M. & Chattopadhyay, S. An experimental analysis of security vulnerabilities in industrial IoT devices. ACM Trans. Internet Technol. 20(1), 1–24 (2020).
Google Scholar
Visoottiviseth, V., Sakarin, P., Thongwilai, J. & Choobanjong T. Signature-based and behavior-based attack detection with machine learning for home IoT devices. In Proc. 2020 IEEE Region 10 Conference (TENCON 2020) 829–834 (2020).
Turk, Z., Soto, B. G. D., Mantha, B. R. K., Maciel, A. & Georgescu, A. A systemic framework for addressing cybersecurity in construction. Autom. Construct. 133(3), 103988 (2022).
Google Scholar
Al Hayajneh, A., Bhuiyan, N. Z. A. & McAndrew, I. Improving internet of things (IoT) security with software defined networking (SDN). Computers 9(1), 8 (2020).
Google Scholar
Hussain, F., Hassan, S. A., Hussain, R. & Hossain, E. Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges. IEEE Commun. Surv. Tutor. 22(2), 1251–1275 (2020).
Google Scholar
IoT Dataset for Intrusion Detection Systems (IDS). (2023).
Nawir, M., Amir, A., Yaakob, N. & Lynn, O. B. Internet of Things (IoT): Taxonomy of security attacks. In Proc. 3rd International Conference in Electronic Design (ICED) 321–326 (2016).
Herzberg, B., Bekerman, D. & Zeifman, I. Breaking down mirai: An IoT DDoS botnet analysis. Incapsula Blog, Bots and DDoS, Security, (2016).
Ambusaidi, M. A., He, X., Nanda, P. & Tan, Z. Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 65(10), 2986–2998 (2016).
Google Scholar
Moustafa, N., Creech, G. & Slay, J. Big data analytics for intrusion detection system: Statistical decision-making using finite Dirichlet mixture models. In Data Analytics and Decision Support for Cybersecurity 1st edn (eds Moustafa, N. et al.) 127–156 (Springer, 2017).
Google Scholar
Tsai, C. F. & Lin, C. Y. A triangle area based nearest neighbors approach to intrusion detection. Pattern Recogn. 43(1), 222–229 (2010).
Google Scholar
Alom, M. Z., Bontupalli, V. & Taha, T. M. Intrusion detection using deep belief networks. In Proc. IEEE National Aerospace and Electronics Conference (NAECON) 339–344 (2015).
Yin, C., Zhu, Y., Fei, J. & He, X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017).
Google Scholar
Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R. & Ghogho, M. Deep learning approach for network intrusion detection in software defined networking. In Proc. 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM) 258–263 (2016).
Ludwig, S. A. Intrusion detection of multiple attack classes using a deep neural net ensemble. In Proc. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) 1–7 (2017).
Al-Hawawreh, M., Moustafa, N. & Sitnikova, E. Identification of malicious activities in industrial Internet of Things based on deep learning models. J. Inf. Secur. Appl. 41, 1–11 (2018).
Shone, N., Ngoc, T. N., Phai, V. D. & Shi, Q. Deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018).
Google Scholar
Subba, B., Biswas, S. & Karmakar, S. Enhancing performance of anomaly-based intrusion detection systems through dimensionality reduction using principal component analysis. In Proc. 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) 1–6 (2016).
Kumar, R. et al. Blockchain-based authentication and explainable AI for securing consumer IoT applications. IEEE Trans. Consumer Electron. (2024).
Google Scholar
Javeed, D., Gao, T., Kumar, P. & Jolfaei, A. An explainable and resilient intrusion detection system for industry 5.0. IEEE Trans. Consumer Electron. 70(1), 1342–1350. (2024).
Google Scholar
Kumar, R. et al. Digital twins-enabled zero touch network: A smart contract and explainable AI integrated cybersecurity framework. Future Gener. Comput. Syst. (2024).
Google Scholar
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