Smart City Gnosys
Smart city article details
| Title | A Hybrid Software-Defined Networking Approach For Enhancing Iot Cybersecurity With Deep Learning And Blockchain In Smart Cities |
|---|---|
| ID_Doc | 2219 |
| Authors | Alotaibi J. |
| Year | 2025 |
| Published | Peer-to-Peer Networking and Applications, 18, 3 |
| DOI | http://dx.doi.org/10.1007/s12083-025-01935-8 |
| Abstract | The IoT has rapidly grown and has changed traditional network connectivity to smart environments that are tightly connected. However, most IoT devices do not have proper security features and hence are susceptible to different types of cyber-attacks. To address these issues, this study has presented a unique design that incorporates Deep Learning (DL), Software Defined Networking (SDN) and Blockchain technology to improve IoT cyber security. The main research question is aimed at identifying the possible approach to creating a cyber-security system for IoT-based smart environments to guarantee data confidentiality, secure financial transactions and proper threat identification. The proposed methodology is a combination of several sophisticated methods. The SDN control plane integrates the Squeeze-Excitation (SE) based Bi-Directional Long Short-Term Memory (SE based Bi-LSTM) model with the Honey Badger Algorithm (HBA) for traffic control. Blockchain is there to enhance the credibility and safety of the data and the transactions within the network. The Synthetic Minority Over-Sampling Technique (SMOTE) is employed to handle the class imbalance problem in the dataset to increase the model’s performance on the imbalanced data. The Bi-LSTM-HBA model is trained and validated on CICIDS 2018 dataset which is a realistic dataset for analyzing and mitigating cyber threats. To assess the efficiency of the proposed Bi-LSTM-HBA model in detecting high and low-frequency cyber threats, it is compared with other classifiers including GRU and BiLSTM. The findings show that the proposed Bi-LSTM-HBA model provides the best performance measures of 99.55% accuracy, 99.36% precision, 99.44% recall and a 99.42% F1-score. From these results, it can be said that the suggested model is very efficient in detecting and preventing cyber threats and surpasses other benchmark classifiers. Therefore, the Bi-LSTM-HBA model is a novel improvement in improving the security of IoT networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. |
| Author Keywords | AI; Bi-LSTM-HBA; Blockchain; Cyber security; Deep learning; Intrusion detection; IoT; SDN; Smart environments; SMOTE; Squeeze-Excitation (SE) |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
40871  | 0.896 | Villegas-Ch W.; Govea J.; Gutierrez R.; Mera-Navarrete A. | Optimizing Security In Iot Ecosystems Using Hybrid Artificial Intelligence And Blockchain Models: A Scalable And Efficient Approach For Threat Detection | IEEE Access, 13 (2025) |
| 23639 | 0.895 | Perumal E.; Arulanthu P.; Ramachandran R.; Singh R. | Enhanced Metaheuristics With Deep Learning Model For Blockchain Assisted Cyber Security Solution In Internet Of Things Environment | 2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024 (2024) |
| 47733 | 0.891 | Pawar P.P.; Kumar D.; Meesala M.K.; Pareek P.K.; Addula S.R.; Shwetha K.S. | Securing Digital Governance: A Deep Learning And Blockchain Framework For Malware Detection In Iot Networks | 2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024 (2024) |
| 29731 | 0.891 | Mishra S.; Chaurasiya V.K. | Hybrid Deep Learning Algorithm For Smart Cities Security Enhancement Through Blockchain And Internet Of Things | Multimedia Tools and Applications, 83, 8 (2024) |
| 36064 | 0.89 | Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I. | Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey | Sensors, 25, 11 (2025) |
| 7407 | 0.89 | Kumar A.; Kumar S. | An Advance Encryption And Attack Detection Framework For Securing Smart Cities Data In Blockchain Using Deep Learning Approach | Wireless Personal Communications, 135, 3 (2024) |
| 47699 | 0.885 | Thamaraiselvi K.; Pushpalatha A.; Chidambarathanu K.; Wankhede J.P.; Alagumuthukrishnan S.; Sarveshwaran V. | Securechainai: Integrating Blockchain And Artificial Intelligence For Enhanced Security In Iot Environments | Proceedings of the 5th International Conference on Smart Electronics and Communication, ICOSEC 2024 (2024) |
| 35041 | 0.885 | Moawad M.M.; Madbouly M.M.; Guirguis S.K. | Leveraging Blockchain And Machine Learning To Improve Iot Security For Smart Cities | Lecture Notes on Data Engineering and Communications Technologies, 164 (2023) |
| 28753 | 0.884 | Alrayes F.S.; Aljebreen M.; Alghamdi M.; Alrslani F.A.F.; Alshuhail A.; Almukadi W.S.; Basheti I.; Sharif M.M. | Harnessing Blockchain With Ensemble Deep Learning-Based Distributed Dos Attack Detection In Iot-Assisted Secure Consumer Electronics Systems | Fractals, 32, 9-10 (2024) |
| 12577 | 0.883 | Almuqren L.; Mahmood K.; Aljameel S.S.; Salama A.S.; Mohammed G.P.; Alneil A.A. | Blockchain-Assisted Secure Smart Home Network Using Gradient-Based Optimizer With Hybrid Deep Learning Model | IEEE Access, 11 (2023) |