International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1742KB)
TriGuard-Net: A Blockchain-enabled Hybrid Encryption and Ensemble Deep Learning Framework for Secure and Intelligent IoT DDoS Detection and Mitigation
PDF (1742KB), PP.70-95
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Author(s)
Index Terms
DDoS Attacks, Homomorphic Encryption, ChaCha20, Dingo Optimizer, TriGuard-Net, Fire Hawks Optimizer
Abstract
In the Internet of Things (IoT) environment, a Distributed Denial-of-Service (DDoS) attack in the network causes poor performance and resource-limited issues to users. Existing systems do not provide real-time adaptability, leading to delayed mitigation. Also, centralized storage systems suffer from breaches and tampering. To tackle these issues, a secure and intelligent IoT DDoS detection and mitigation framework is presented that utilizes hybrid encryption, blockchain storage, ensemble deep learning (DL), and reinforcement learning (RL) to improve the accuracy, security, and efficiency of IoT networks against several cyber-attacks. The developed technique collects data from a dataset and pre-processes it for handling missing values and normalizes it for further analysis. Secondly, a hybrid encryption method combining Homomorphic Encryption (HE) and ChaCha20 is adopted for data encryption with optimal key selection using Dingo Optimizer (DOX). Then, the encrypted data is securely stored in blockchain through off-chain storage and on-chain hash storage to ensure data integrity and tamper-proof security. DDoS attack detection is performed using an ensemble model called TriGuard-Net that combines AlexNet, LSTM, and PSPNet, with optimizing hyperparameters using Fire Hawks Optimizer (FHO). Finally, an RL-based mitigation system using Deep Q-Network (DQN) helps in real-time attack mitigation and enhances IoT security. Experimental results reveal that the presented model offers superior performance by achieving an accuracy of 99%, a kappa score of 98%, an R2 Score of 97%, an MCC of 98%, a Jaccard Score of 98%, and a Hamming Loss of 0.006, thereby outperforming other current models.
Cite This Paper
Dhanya M. Rajan, D. John Aravindhar, "TriGuard-Net: A Blockchain-enabled Hybrid Encryption and Ensemble Deep Learning Framework for Secure and Intelligent IoT DDoS Detection and Mitigation", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.3, pp.70-95, 2026. DOI:10.5815/ijitcs.2026.03.06
Reference
[1]A. A. Najar, M. N. Sugali, F. R. Lone, and A. Nazir, “A novel CNN-based approach for detection and classification of DDoS attacks,” Concurrency and Computation: Practice and Experience, vol. 36, no. 19, Art. no. e8157, 2024.
[2]R. Gopi, V. Sathiyamoorthi, S. Selvakumar, R. Manikandan, P. Chatterjee, N. Z. Jhanjhi, and A. K. Luhach, “Enhanced method of ANN-based model for detection of DDoS attacks on multimedia Internet of Things,” Multimedia Tools and Applications, pp. 1–19, 2022.
[3]S. A. Khanday, H. Fatima, and N. Rakesh, “Implementation of intrusion detection model for DDoS attacks in lightweight IoT networks,” Expert Systems with Applications, vol. 215, Art. no. 119330, 2023.
[4]A. Yazdinejad, A. Dehghantanha, R. M. Parizi, G. Srivastava, and H. Karimipour, “Secure intelligent fuzzy blockchain framework: Effective threat detection in IoT networks,” Computers in Industry, vol. 144, Art. no. 103801, 2023.
[5]A. Kumar and D. Singh, “Detection and prevention of DDoS attacks on edge computing of IoT devices through reinforcement learning,” International Journal of Information Technology, vol. 16, no. 3, pp. 1365–1376, 2024.
[6]R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, S. Garg, and M. M. Hassan, “A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network,” Journal of Parallel and Distributed Computing, vol. 164, pp. 55–68, 2022.
[7]B. Ilyas, A. Kumar, M. A. Setitra, Z. A. Bensalem, and H. Lei, “Prevention of DDoS attacks using an optimized deep learning approach in blockchain technology,” Transactions on Emerging Telecommunications Technologies, vol. 34, no. 4, Art. no. e4729, 2023.
[8]A. Binbusayyis and T. Vaiyapuri, “Identifying and benchmarking key features for cyber intrusion detection: An ensemble approach,” IEEE Access, vol. 7, pp. 106495–106513, 2019.
[9]Y. B. Abushark, A. I. Khan, F. Alsolami, A. Almalawi, M. M. Alam, A. Agrawal, R. Kumar, and R.A. Khan, “Cyber security analysis and evaluation for intrusion detection systems,” Computers, Materials & Continua, vol. 72, no. 1, pp. 1765–1783, 2022.
[10]P. García, J. de Curtò, I. de Zarzà, J. C. Cano, and C. T. Calafate, “Foundation models for cybersecurity: A comprehensive multi-modal evaluation of TabPFN and TabICL for tabular intrusion detection,” Electronics, vol. 14, no. 19, p. 3792, 2025.
[11]A. D. Aguru and S. B. Erukala, “A lightweight multi-vector DDoS detection framework for IoT-enabled mobile health informatics systems using deep learning,” Information Sciences, vol. 662, Art. no. 120209, 2024.
[12]R. Baskerville and V. Vaishnavi, “A novel approach to collectively determine cybersecurity performance benchmark data: Aiding organizational cybersecurity assessment,” in Design Science Research. Cases, Cham, Switzerland: Springer International Publishing, 2020, pp. 17–41.
[13]S. Sumathi, R. Rajesh, and S. Lim, “Recurrent and deep learning neural network models for DDoS attack detection,” Journal of Sensors, vol. 2022, Art. no. 8530312, 2022.
[14]S. Tharewal, M. W. Ashfaque, S. S. Banu, P. Uma, S. M. Hassen, and M. Shabaz, “Intrusion detection system for industrial Internet of Things based on deep reinforcement learning,” Wireless Communications and Mobile Computing, vol. 2022, Art. no. 9023719, 2022.
[15]Y. Zhang, D. Zhu, M. Wang, J. Li, and J. Zhang, “A comparative study of cyber security intrusion detection in healthcare systems,” International Journal of Critical Infrastructure Protection, vol. 44, p. 100658, 2024.
[16]M. T. Hussan, G. V. Reddy, P. T. Anitha, A. Kanagaraj, and P. Naresh, “DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization,” Cluster Computing, vol. 27, no. 4, pp. 4469–4490, 2024.
[17]M. Aljebreen, H. A. Mengash, M. A. Arasi, S. S. Aljameel, A. S. Salama, and M. A. Hamza, “Enhancing DDoS attack detection using snake optimizer with ensemble learning on Internet of Things environment,” IEEE Access, vol. 11, pp. 104745–104753, 2023.
[18]D. Akgun, S. Hizal, and U. Cavusoglu, “A new DDoS attacks intrusion detection model based on deep learning for cybersecurity,” Computers & Security, vol. 118, Art. no. 102748, 2022.
[19]V. Brindha Devi, N. M. Ranjan, and H. Sharma, “IoT attack detection and mitigation with optimized deep learning techniques,” Cybernetics and Systems, vol. 55, no. 7, pp. 1702–1728, 2024.
[20]M. Alkhammash, “A metaheuristic approach to detecting and mitigating DDoS attacks in blockchain-integrated deep learning models for IoT applications,” IEEE Access, 2024.
[21]I. Katib and M. Ragab, “Blockchain-assisted hybrid Harris Hawks optimization-based deep DDoS attack detection in the IoT environment,” Mathematics, vol. 11, no. 8, Art. no. 1887, 2023.
[22]A. Ahmim, F. Maazouzi, M. Ahmim, S. Namane, and I. B. Dhaou, “Distributed denial of service attack detection for the Internet of Things using hybrid deep learning model,” IEEE Access, vol. 11, pp. 119862–119875, 2023.
[23]N. U. Ain, M. Sardaraz, M. Tahir, M. W. Abo Elsoud, and A. Alourani, “Securing IoT networks against DDoS attacks: A hybrid deep learning approach,” Sensors, vol. 25, no. 5, Art. no. 1346, 2025.
[24]S. Siddharth, “DDoS botnet attack on IoT devices,” Kaggle Dataset, 2022. [Online]. Available: https://www.kaggle.com/datasets/siddharthm1698/ddos-botnet-attack-on-iot-devices
[25]A. Fadlil, “K-nearest neighbor imputation performance on missing value data graduate user satisfaction,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 4, pp. 570–576, 2022.
[26]I. MZada, E. Omran, S. Jan, H. Alfraihi, S. Alsalamah, A. Alshahrani, S. Hayat, and N. Phi, “Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection,” PLOS ONE, vol. 20, no. 7, p. e0328050, 2025.
[27]A. Ali, H. Ali, A. Saeed, A. A. Khan, T. T. Tin, M. Assam, and H. G. Mohamed, “Blockchain-powered healthcare systems: Enhancing scalability and security with hybrid deep learning,” Sensors, vol. 23, no. 18, Art. no. 7740, 2023.
[28]F. A. Fadhil, F. T. A. H. Alhilo, and M. T. Abdulhadi, “Enhancing data security using Laplacian of Gaussian and ChaCha20 encryption algorithm,” Journal of Intelligent Systems, vol. 33, no. 1, Art. no. 20240191, 2024.
[29]A. K. Bairwa, S. Joshi, and D. Singh, “Dingo optimizer: A nature-inspired metaheuristic approach for engineering problems,” Mathematical Problems in Engineering, vol. 2021, Art. no. 2571863, 2021.
[30]J. Saikam and K. Ch, “DeepSecure Net: An ensemble DPATMFNet approach with enhanced feature selection for advanced SDN network intrusion detection system,” 2024.
[31]A. Thangasamy, B. Sundan, and L. Govindaraj, “A novel framework for DDoS attacks detection using hybrid LSTM techniques,” Computer Systems Science & Engineering, vol. 45, no. 3, 2023.
[32]X. Qi, J. Dong, Y. Lan, and H. Zhu, “Method for identifying litchi picking position based on YOLOv5 and PSPNet,” Remote Sensing, vol. 14, no. 9, Art. no. 2004, 2022.
[33]M. Azizi, S. Talatahari, and A. H. Gandomi, “Fire Hawk optimizer: A novel metaheuristic algorithm,” Artificial Intelligence Review, vol. 56, no. 1, pp. 287–363, 2023.
[34]S. P. Praveen, A. Chokka, P. Sarala, R. Nakka, S. B. Chandolu, and V. E. Jyothi, “Investigating the efficacy of deep reinforcement learning models in detecting and mitigating cyber-attacks: A novel approach,” Journal of Cybersecurity & Information Management, vol. 14, no. 1, 2024.
[35]A. P. Gopi, M. Gowthami, T. Srujana, S. G. Padmini, and M. D. Malleswari, “Classification of denial-of-service attacks in IoT networks using AlexNet,” in Proc. ICHCSC, Singapore: Springer Nature, 2022, pp. 349–357.
[36]V. Hnamte, A. A. Najar, H. Nhung-Nguyen, J. Hussain, and M. N. Sugali, “DDoS attack detection and mitigation using deep neural network in SDN environment,” Computers & Security, vol. 138, Art. no. 103661, 2024.
[37]Y. Alhasawi and S. Alghamdi, “Federated learning for decentralized DDoS attack detection in IoT networks,” IEEE Access, vol. 12, pp. 42357–42368, 2024.
[38]M. F. Saiyed and I. Al-Anbagi, “Deep ensemble learning with pruning for DDoS attack detection in IoT networks,” IEEE Transactions on Machine Learning in Communications and Networking, 2024.