Volume 14 • Issue 2 • PP: 198-213 • 2024
Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm
View PDF Abstract
In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.
Keywords
References
[1] Zheng, H., Ni, L. and Xiao, D. “Intrusion Detection based on MLP Neural Networks and K-means Algorithm,” in Proceedings of Advances in Neural Networks, Springer Berlin, Vol. 3498, pp. 434-438, 2005.
[2] Yasami, Y. and Mozaffari, S.P. “A Novel Unsupervised Classification Approach for Network Anomaly Detection by K-means Clustering and ID3 Decision Tree Learning Methods”, in the Journal of Supercomputing, Springer Netherlands, Vol. 53, No. 1, pp. 231-245, 2010.
[3] A., M. M., N. "A Multi-level Features Fusion Model for Network Communication based on Machine Learning," Journal of International Journal of Wireless and Ad Hoc Communication, vol. 5, no. 1, pp. 36-43, 2022. DOI: https://doi.org/10.54216/IJWAC.050103
[4] Xuan, Y., Shin, I., Thai, M. and Znati, T. “Detecting Application Denial-Of-Service Attacks: A Group-Testing-Based Approach”, IEEE Transaction on Parallel and Distributed Systems, Vol. 21, pp. 1022-1031, 2010.
[5] Srivastava, A. K., P. "Fuzzy Logic Based Load Balanced Clustering for Network Lifetime Enhancement in WSN," Journal of International Journal of Wireless and Ad Hoc Communication, vol. 7, no. 1, pp. 08-17, 2023. DOI: https://doi.org/10.54216/IJWAC.070101.
[6] Aggarwal, C.C. and Yu, S.P. “Finding Generalized Projected Clusters in High Dimensional Spaces”, in Proceedings of the ACM SIGMOD Conference, Vol. 29, No. 2, pp. 70-81, 2000.
[7] Chirillo, J. “Network Security for Windows, UNIX and Linux Networks: Hack Attacks Denied”, Wiley Publishing Inc., 2nd Edition, 2002.
[8] Chou, T. and Chou, T. “Hybrid Classifier Systems for Intrusion Detection”, in 7th Annual Communication Networks and Services Research Conference, pp. 286-291, 2009.
[9] Chowdhury, N. and Murthy, C.A. “Minimum Spanning Tree Based Clustering Techniques: Relationship with Bayes Classifier”, Pattern Recognition, Vol. 30, No. 11, pp. 1919-1929, 1997.
[10] Krishnamoorthi, Subba Reddy, N.V. and Dinesh Acharya, U. “A Two-Stage Hybrid Model For Intrusion Detection”, in IEEE Journal on Computer Networks, pp. 163-165, 2006.
[11] Lam, H.K., Ling, S.H., Leung, F.H.F. and Tam, P.K.S. “Tuning of the Structure and Parameters of Neural Network using an Improved Genetic Algorithm,” in Proceedings of the 27th Annual Conference of the IEEE Industrial Electronics
[12] Luo, L., Ye, L., Luo, M., Huang, D., Peng, H. and Yang, F. “Methods of Forward Feature Selection Based on the Aggregation of Classifiers Generated by Single Attribute”, Computers in Biology and Medicine, Vol. 41, No. 7, pp. 435-441, 2011.
[13] Ma, P.C.H. and Chan, K.C.C. “An Iterative Data Mining Approach for Mining Overlapping Coexpression Patterns in Noisy Gene Expression”, in IEEE Transactions on Nano Bioscience, Vol. 8, No. 3, pp. 252-258, 2009
[14] Mulay, S.A., Devale, P.R. and Garje, G.V. “Decision Tree based Support Vector Machine for Intrusion Detection”, International Conference on Networking and Information Technology, pp. 59-63, 2010.
[15] Nauta, K.R. and Lieble, F. “Offline Network Intrusion Detection: Mining TCPDUMP Data to Identify Suspicious Activity”, in Proceedings of the AFCEA Federal Database Colloquium, pp. 1-19, 1999.
[16] Pan, Z., Chen, S., Hu, G. and Zhang, D. “Hybrid Neural Network and C4.5 for Misuse Detection”, in Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, Vol. 4, pp. 2463-2467, 2003.
[17] Weon, Y., Song, D.H., Lee, C., Heo, Y. and Kim, K. “A Memorybased Learning Approach to Reduce False Alarms in Intrusion Detection”, in 7th IEEE International Conference on Advanced Communication Technology (ICACT), pp. 241-245, 2005.
[18] Xiang, C. and Lim, S.M. “Design of Multiple-level Hybrid Classifier for Intrusion Detection System”, IEEE Transactions on System, Man, Cybernetics, Part A, Cybernetics, Vol. 2, No. 28, pp. 117-122, 2002
[19] Xu, Y., and Chow, T “Efficient Self-Organizing Map Learning Scheme using Data Reduction Pre-Processing”, in Proceedings of the World Congress on Engineering, Vol. 1, No. 3, pp. 542, 2010.
[20] S. Rajasoundaran et al., "Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks," Computer Communications, vol. 187, pp. 71-82, 2022
Cite This Article
Choose your preferred format