Sharma, A., Singh, P.K. and Kumar, Y. Integrated Fire Detection System using IoT and Image Processing Technology for Smart Cities. Sustain. Cities Society 61e4826 (2020).
Sinan, K. SDG-11: Sustainable Cities and Communities. Emerging Technologies, Sustainable Development Goals Series 1st edition (Springer, 2020).
Google Academic
Hussain, F., Hussain, R., Hassan, S.A., Hossain, E. Machine learning in IoT security: current solutions and future challenges. IEEE Communications Research Lecturer. twenty two(3), 1686–1721 (2020).
Bharati, S., Mondal, MRH, Podder, P. & Prasath, V.B. Federated learning: Applications, challenges and future directions. International Journal of Hybrid Intelligence Systems 18(1–2), 19–35 (2022).
Google Academic
Shafiq, M., Tian, Z., Bashir, A.K., Du, X., Guizani, M. Corrauc: A method for detecting malicious BOT-IOT traffic in IoT networks using machine learning techniques. IEEE Internet Things J. 8(5), 3242–3254 (2020).
Omolala, A.E. etc Security in the Internet of Things: A comprehensive survey of unexplored territory and new insights. Computer Security 112102494 (2022).
Bharati, S., Podder, P., Mondal, MRH & Paul, PK Applications and Challenges of Cloud Integrated IoMT. Cognitive Internet of Medical Things for Smart Healthcare 1st ed. (editor Hassanien, A.E. etc) 67–85 (Springer, 2021).
Ozalp, A.N. othersA layer-based study of cyber attacks in IoT. 2022 International Conference on Human-Computer Interaction, Optimization and Robotics Applications (HORA) (IEEE, 2022).
Altunay, H. C. & Albayrak, Z. A hybrid CNN+ LSTM based intrusion detection system for industrial IoT networks. International Journal of Engineering Science and Technology 38101322 (2023).
Google Academic
Abbas, Y., Ali, D., Gautam, S., Hadis, K. and Reza, M. P. Hybrid privacy-preserving federated learning for irregular users in next generation internet of things. J. Syst. Arch. 148103088 (2024).
Abbas, Y., Ali, D., Gautam, S. AP2FL: An Auditable Privacy Preserving Federated Learning Framework for Electronic Devices in Healthcare. IEEE Trans. Consumer Electron. 991 (2023).
Google Academic
Danyal, N., Abbas, Y., Ali, D. and Gautam, S. A federated quantum-based privacy-preserving threat detection model for consumer IoT. IEEE Trans. Consumer Electron. https://doi.org/10.1109/TCE.2024.3377550 (2024).
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. 2021 18th International Conference on Privacy, Security and Trust (PST), IEEE Xplore (2021).
Abbas, Y., Ali, D., Reza, M. P., Gautam, S., Hadis, K. A Secure and Intelligent Fuzzy Blockchain Framework: Effective Threat Detection in IoT Networks. Computer Industry 144103801 (2023).
Gopi, K.J., Abbas, Y., Reza, M.P., Seyedamin, P. Exploring privacy measures in federated learning. J. Supercomputer. 143 (2023).
Google Academic
Otoum, Y. & Nayak, A. IoT security from a deep learning perspective. Proceedings of the 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 Communications Research Lecturer. twenty two(1), 616–644 (2020).
Tahsien, S. M., Karimipour, H. & Spachos, P. Machine learning based solutions for Internet of Things (IoT) security: A survey. J. Netw. Comput. Appl. 161102630 (2020).
Abiodun, OI, Abiodun, EO, Alawida, M., Alkhawaldeh, RS & Arshad, H. A review of Internet of Things security: challenges and solutions. Wire. Person. Communication. 119(3), 2603–2637 (2021).
Podder, P., Mondal, MRH, Bharati, S., Paul, PK A review of security threats in the Internet of Things. International Journal of Computing Applications 176(41), 37–45 (2020).
Google Academic
Hamad, Z.J. & Askar, S. Machine learning powered IoT for smart applications. International Science Journal Bus Five(3), 92–100 (2021).
Google Academic
Xu, H. etc A combined strategy of integrated optimization algorithm and weighted K-nearest neighbors based feature selection to improve the performance of network intrusion detection. electronics 9(8), 1206 (2020).
Bharati, S. & Mondal, MRH Computational intelligence for pandemic management. 12 Applications and Challenges of AI-Driven IoHT for Pandemic Preparedness: A Review (Bharati, S. & Mondal, MRH eds.) 213–230 (De Gruyter, 2021).
Google Academic
Robel, MRA, Bharati, S., Podder, P. & Mondal, MRH IoT Driven Healthcare Monitoring System. Fog, Edge and Pervasive Computing in Intelligent IoT-Driven Applications (Eds: Gupta, D. & Kamparia, A.) 161–176 (Wiley, 2020).
Podder, P., Mondal, MRH & Kamruzzaman, J. Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern. Applications of computational intelligence in multidisciplinary research 1st ed. (Eds. Elngar, A.A. etc) (Elsevier, 2022).
Google Academic
Chandavarkar, B. R. Hardcoded Credentials and Insecure Data Transfer in IoT: National and International Context. Proceedings of the 2020 11th International Conference on Computing, Communications, and Networking Technologies (ICCCNT) 1–7 (2020).
Ferrara, P., Mandal, A.K., Cortesi, A., Spoto, F. Static analysis for discovering IoT vulnerabilities. Int. J. Software Tools Technol. Trans. twenty three(1), 71–88 (2021).
Yu, Y., Guo, L., Liu, S., Zheng, J., Wang, H. A privacy preserving scheme based on CP-ABE in crowdsourcing IoT for smart ocean. IEEE Internet Things J. 7(10), 10061–10071 (2020).
Xiong, J. etc A personalized privacy-preserving framework for mobile crowdsensing in the IIoT. IEEE Trans.Ind.Inform. Translation 16(6), 4231–4241 (2020).
Jiang, X., Lora, M., Chattopadhyay, S. An Empirical Analysis of Security Vulnerabilities in Industrial IoT Devices. ACM Trans. Internet Technol. 20(1), 1–24 (2020).
Visoottiviseth, V., Sakarin, P., Thongwilai, J., Choobanjong T. Signature- and behavior-based attack detection for home IoT devices using machine learning. Proceedings of the 2020 IEEE Region 10 Conference (TENCON 2020) 829–834 (2020).
Turk, Z., Soto, BGD, Mantha, BRK, Maciel, A., Georgescu, A. A systematic framework for addressing cybersecurity in the construction industry. Autom. Build it. 133(3), 103988 (2022).
Al Hayajneh, A., Bhuiyan, NZA, McAndrew, I. Improving Internet of Things (IoT) Security with Software Defined Networking (SDN). computer 9(1), 8 (2020).
Hussain, F., Hassan, S.A., Hussain, R., Hossain, E. Machine learning for resource management in cellular and IoT networks: Opportunities, current solutions, and open challenges. IEEE Communications Research Lecturer. twenty two(2), 1251–1275 (2020).
IoT Dataset for Intrusion Detection Systems (IDS). https://www.kaggle.com/azalhowaide/iot-dataset-for-intrusion-detection-systems-ids (2023).
Nawir, M., Amir, A., Yaakob, N. & Lynn, O.B. Internet of Things (IoT): A taxonomy of security attacks. Proceedings of the 3rd International Conference on Electronics Design (ICED) 321–326 (2016).
Herzberg, B., Bekerman, D., Zeifman, I. “Analyzing 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).
Moustafa, N., Creech, G., Slay, J. Big data analytics for intrusion detection systems: statistical decision making using finite Dirichlet mixture models. Data analytics and decision support for cybersecurity 1st Edition (Editor: Moustafa, N. etc) 127–156 (Springer, 2017).
Tsai, CF & Lin, CY A triangular region based nearest neighbor approach to intrusion detection. Pattern recognition. 43(1), 222-229 (2010).
Alom, M. Z., Bontupalli, V. & Taha, T. M. Intrusion detection using deep belief networks. Proceedings of the IEEE National Aerospace 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 Five21954–21961 (2017).
Tang, TA, Mhamdi, L., McLernon, D., Zaidi, SAR & Ghogho, M. A deep learning approach for network intrusion detection in software defined networks. Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM) 258–263 (2016).
Ludwig, S. A. Intrusion detection for multiple attack classes using deep neural net ensembles. Proceedings of the 2017 IEEE Symposium on Computational Intelligence (SSCI) 1–7 (2017).
Al-Hawawreh, M., Moustafa, N., Sitnikova, E. Identifying malicious activities in the industrial IoT based on deep learning models. J. Inf. Secur. Appl. 411–11 (2018).
Google Academic
Shone, N., Ngoc, TN, Phai, VD, Shi, Q. A deep learning approach to network intrusion detection. IEEE Trans. Urgent. On Computing. Intelligence. 2(1), 41–50 (2018).
Subba, B., Biswas, S., Karmakar, S. Improving the performance of anomaly based intrusion detection systems through dimensionality reduction using principal component analysis. Proceedings of the 2016 IEEE International Conference on Advanced Networking and Communication Systems (ANTS) 1–6 (2016).
Kumar, R. etc Blockchain-based authentication and explainable AI for securing consumer IoT applications. IEEE Trans. Consumer Electron. https://doi.org/10.1109/TCE.2023.3320157 (2024).
Javeed, D., Gao, T., Kumar, P., Jolfaei, A. Explainable and Resilient Intrusion Detection System for Industry 5.0. IEEE Trans. Consumer Electron. 70(1), 1342–1350. https://doi.org/10.1109/TCE.2023.3283704 (2024).
Kumar, R. etc Digital Twin-Enabled Zero Touch Network: A Cybersecurity Framework Integrating Smart Contracts and Explainable AI. Future Generation Computer Systems https://doi.org/10.1016/j.future.2024.02.015 (2024).
