Volume 15 • Issue 1 • PP: 151-165 • 2025
A Multiclass Attack Classification Framework for IoT Using Hybrid Deep Learning Model
View PDF Abstract
In recent years, the Internet of Things (IoT) has emerged as one of the most significant concepts in numerous facets of our contemporary way of life. Nonetheless, addressing the concerns over the IoT's security presents the most significant obstacle to the widespread adoption of this technology. Using an Intrusion Detection System (IDS) to detect malicious activity in the networks is one of the most essential things that can be done to solve the security concerns posed by the IoT. Hence, a Deep Learning-based IDS (DL-IDS) model is designed for the multi-class classification of attacks in the IoT networks. This DL-IDS model includes data preprocessing, feature extraction, feature selection, and classification processes. The Bot-IoT and IoT-23 datasets are used as input for the research model. In preprocessing, the datasets are normalized, and the missing data are replaced. After preprocessing, the features are extracted using the Convolutional Neural Network (CNN) architecture. The features selection process is performed from the extracted features by implementing the Quantum-based Chameleon Swarm Optimization (QCSO) algorithm, which selects features from the datasets. Based on these features selected, the multi-class classification is carried out using the Deep Belief Network (DBN) for each attack presented in the datasets. The classification performance is performed individually for both datasets and evaluated using accuracy, detection rate, precision, and f1-scores. The performances of the proposed DL-IDS model are compared with the other models analyzed from the literature survey discussed in this work. The average scores obtained using the IoT-23 data set include 99.45% accuracy, 99.47% detection rate, 99.66% f1-scores, and 99.85% precision. For the Bot-IoT data, the average scores are 99.49% accuracy, 99.52% detection rate, 99.70% f1-score, and 99.88% precision.
Keywords
References
[1] A. Khraisat and A. Alazab, “A critical review of intrusions detections system in the internet of things: techniques, deployment strategy, validation strategy, attack, public dataset and challenge,” Cybersecurity, vol. 4, no. 18, pp. 1-27, 2021.
[2] A. A. Anitha and L. Arockiam, “A Review on Intrusions Detections System to Secure IoT Network,” International Journal of Computer Networks and Applications, vol. 9, no. 10, pp. 38-50, 2022.
[3] E. Gyamfi and A. Jurcutt, “Intrusions Detections in Internet of Things System: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Dataset,” Sensors, vol. 22, no. 3744, pp. 1-33, 2022.
[4] Kumar, I., Kumar, A., Kumar, V.D.A. et al. Dense Tissue Pattern Characterization Using Deep Neural Network. Cogn Comput (2022). https://doi.org/10.1007/s12559-021-09970-2.
[5] A. R. Khan, M. Kashif, R. H. Jhaveri, R. Raut, T. Saba, and S. A. Bahaj, “Deep Learning for Intrusions Detections and Security of Internet of Things (IoT): Current Analysis, Challenge, and Possible Solution,” Security and Communication Networks, vol. 2022, 4016073, pp. 1-13, 2022.
[6] S. Baniasadi, O. Rostammi, D. Marttín, and M. Kavah, “A Novel Deep Supervised Learning-Based Approach for Intrusions Detections in IoT System,” Sensors, vol. 22, no. 4459, pp. 1-17, 2022.
[7] A. Fatani, A. Dahau, M. A. A. Al-qanes, S. Lui, and M. A. Elaaziz, “Advanced Features Extractions and Selections Approach Using Deep Learning and Aquila Optimizers for IoTs Intrusions Detections Systems,” Sensors, vol. 22, no. 140, pp. 1-20, 2022.
[8] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi. (2023). Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification. Journal of Fusion: Practice and Applications, 11 ( 2 ), 90-110.
[9] I. Ullah and Q. H. Mahmoud, “Design and Developments of a Deep Learning-Based Model for Anomaly Detections in IoT Network,” IEEE Access, vol. 9, pp. 103906-103926, 2021.
[10] I. Ullah and Q. H. Mahmoud, “Design and Developments of RNN Anomaly Detections Models for IoT Network,” IEEE Access, vol. 10, pp. 62722-62750, 2022.
[11] Malathi S, Arockia Raj Y, Abhishek Kumar, V D Ashok Kumar, Ankit Kumar, Elangovan D, V D Ambeth Kumar*, Chitra B & a Abirami (2021) Prediction of cardiovascular disease using deep learning algorithms to prevent COVID 19, Journal of Experimental & Theoretical Artificial Intelligence, DOI: 10.1080/0952813X.2021.1966842
[12] N. Abdalgawad, A. Sajjun, Y. Kadoura, I. A. Zualkkernan, and F. Aloal, “Generative Deep Learning to Detect Cyberattack for the IoT-23 Datasets,” IEEE Access, vol. 10, pp. 6430-6441, 2022.
[13] A. Dahou et al., “Intrusions Detections Systems for IoT Based on Deep Learning and Modified Reptile Search Algorithm,” Computational Intelligence and Neuroscience, vol. 2022, 6473507, 2022.
[14] I. Ullah, A. Ullah, and M. Sajjad, “Towards a Hybrid Deep Learning Model for Anomalous Activities Detections in Internet of Things Network,” IoT, vol. 2, pp. 428–448, 2021.
[15] Ruphitha, S.V., Ambeth Kumar, V.D., “ Predictive analysis of postpartum haemorrhage using deep learning technique”, Advances in Parallel Computing, 2021, 38, pp. 168–172.
[16] Sherubha, “Graph Based Event Measurement for Analyzing Distributed Anomalies in Sensor Networks”, Sådhanå(Springer), 45:212, https://doi.org/10.1007/s12046-020-01451-w
[17] Piyush K. Pareek, Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning “,Fusion: Practice and Applications, Volume 2 , Issue 1 , PP: 42-60, 2020
[18] Shivam Grover, Kshitij Sidana, Vanita Jain, “Egocentric Performance Capture: A Review”, Fusion: Practice and Applications, Volume 2, Issue 2 , PP: 64-73, 2020.
[19] Abdel Nasser H. Zaied, Mahmoud Ismail and Salwa El- Sayed, A Survey on Meta-heuristic Algorithms for Global Optimization Problems, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 48-60, 2020
[20] Mahmoud H.Alnamoly, Ahmed M. Alzohairy, Ibrahim M. El-Henawy, “A survey on gel images analysis software tools, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 40-47, 2021.
[21] M. A. Elaziz et al., “A Quantum-Based Chameleon Swarm for Features Selection,” Mathematics, vol. 10, no. 3606, pp. 1-17, 2022.
[22] A. A. Süzen, “Developing a multi‑level intrusions detections systems using hybrid‑DBN,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 1913–1923, 2021.
[23] A. Jahanjoo, M. Naderan and M. J. Rashti, “Detection and multi‑class classifications of falling in elderly people by deep belief networks algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 4145–4165, 2020.
Cite This Article
Choose your preferred format