A Hybrid Approach for Intrusion Detection in the Internet of Things Using Harris Hawks Optimization and Deep Learning Algorithms
محورهای موضوعی : Majlesi Journal of Telecommunication Devices
Reza Kohan
1
,
Hamid Barati
2
,
Ali Barati
3
1 - دانشکده هوش مصنوعی و فناوری های اجتماعی و پیشرفته، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
2 - دانشکده هوش مصنوعی و فناوری های اجتماعی و پیشرفته، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
3 - دانشکده هوش مصنوعی و فناوری های اجتماعی و پیشرفته، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
کلید واژه: Intrusion Detection, Internet of Things, Harris Hawks Optimization, Neural Network, Learning Automata,
چکیده مقاله :
Intrusion detection in Internet of Things (IoT)-based smart cities is essential due to the increasing volume and complexity of cyberattacks. Traditional detection systems face two major challenges: achieving high accuracy and minimizing false alarms, particularly in large-scale and heterogeneous IoT networks. This paper proposes a novel hybrid intrusion detection system that combines Harris Hawks Optimization (HHO) for feature selection with a multi-layer neural network enhanced by learning automata for adaptive classification. The HHO algorithm efficiently reduces input dimensionality by selecting the most relevant features, while the learning automata optimize the network's weights dynamically, improving training stability and robustness. The proposed system is evaluated using the KDDCup99 dataset under both binary and multiclass scenarios. Experimental results show an average accuracy of 96.53%, a true positive rate (TPR) of 94.91%, and a false positive rate (FPR) of 2.80%. Compared to recent baseline models, the proposed method demonstrates superior performance in accuracy and false alarm reduction, confirming its suitability for real-time intrusion detection in dynamic IoT-based environments.
Intrusion detection in Internet of Things (IoT)-based smart cities is essential due to the increasing volume and complexity of cyberattacks. Traditional detection systems face two major challenges: achieving high accuracy and minimizing false alarms, particularly in large-scale and heterogeneous IoT networks. This paper proposes a novel hybrid intrusion detection system that combines Harris Hawks Optimization (HHO) for feature selection with a multi-layer neural network enhanced by learning automata for adaptive classification. The HHO algorithm efficiently reduces input dimensionality by selecting the most relevant features, while the learning automata optimize the network's weights dynamically, improving training stability and robustness. The proposed system is evaluated using the KDDCup99 dataset under both binary and multiclass scenarios. Experimental results show an average accuracy of 96.53%, a true positive rate (TPR) of 94.91%, and a false positive rate (FPR) of 2.80%. Compared to recent baseline models, the proposed method demonstrates superior performance in accuracy and false alarm reduction, confirming its suitability for real-time intrusion detection in dynamic IoT-based environments.
Allioui, H., & Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19), 8015.
Ahmad, R., & Alsmadi, I. (2021). Machine learning approaches to IoT security: A systematic literature review. Internet of Things, 14, 100365.
Hassan, W. H. (2019). Current research on Internet of Things (IoT) security: A survey. Computer networks, 148, 283-294.
He, K., Kim, D. D., & Asghar, M. R. (2023). Adversarial machine learning for network intrusion detection systems: A comprehensive survey. IEEE Communications Surveys & Tutorials, 25(1), 538-566.
Islam, N., Farhin, F., Sultana, I., Kaiser, M. S., Rahman, M. S., Mahmud, M., ... & Cho, G. H. (2021). Towards machine learning based intrusion detection in IoT networks. Comput. Mater. Contin, 69(2), 1801-1821.
Alsoufi, M. A., Razak, S., Siraj, M. M., Nafea, I., Ghaleb, F. A., Saeed, F., & Nasser, M. (2021). Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review. Applied sciences, 11(18), 8383.
Alsheavi, A. N., Hawbani, A., Wang, X., Othman, W., Zhao, L., Liu, Z., ... & Al-qaness, M. A. (2024). IoT Authentication Protocols: Classification, Trend and Opportunities. IEEE Transactions on Sustainable Computing.
Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering, 1-19.
Khan, M. A., Khan, M. A., Jan, S. U., Ahmad, J., Jamal, S. S., Shah, A. A., ... & Buchanan, W. J. (2021). A deep learning-based intrusion detection system for MQTT enabled IoT. Sensors, 21(21), 7016.
Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T., Adebisi, B., & Gacanin, H. (2021). A novel intrusion detection method based on lightweight neural network for internet of things. IEEE Internet of Things Journal, 9(12), 9960-9972.
Wahab, O. A. (2022). Intrusion detection in the iot under data and concept drifts: Online deep learning approach. IEEE Internet of Things Journal, 9(20), 19706-19716.
Vishwakarma, M., & Kesswani, N. (2022). DIDS: A Deep Neural Network based real-time Intrusion detection system for IoT. Decision Analytics Journal, 5, 100142.
Chatterjee, S., & Hanawal, M. K. (2022). Federated learning for intrusion detection in IoT security: a hybrid ensemble approach. International Journal of Internet of Things and Cyber-Assurance, 2(1), 62-86.
Ngo, D. M., Lightbody, D., Temko, A., Pham-Quoc, C., Tran, N. T., Murphy, C. C., Popovici, E. (2023). HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security. Future Internet, 15(1), 9.
Awajan, A. (2023). A novel deep learning-based intrusion detection system for IOT networks. Computers, 12(2), 34.
Wang, Z., Li, J., Yang, S., Luo, X., Li, D., & Mahmoodi, S. (2024). A lightweight IoT intrusion detection model based on improved BERT-of-Theseus. Expert Systems with Applications, 238, 122045.
Qaddos, A., Yaseen, M. U., Al-Shamayleh, A. S., Imran, M., Akhunzada, A., & Alharthi, S. Z. (2024). A novel intrusion detection framework for optimizing IoT security. Scientific Reports, 14(1), 21789.
Wang, N., Shi, S., Chen, Y., Lou, W., & Hou, Y. T. (2025). FeCo: Boosting intrusion detection capability in IoT networks via contrastive learning. IEEE Transactions on Dependable and Secure Computing.
Lin, L., Zhong, Q., Qiu, J., & Liang, Z. (2025). E-GRACL: an IoT intrusion detection system based on graph neural networks. The Journal of Supercomputing, 81(1), 42.
Rahman, M. A., Asyhari, A. T., Leong, L. S., Satrya, G. B., Tao, M. H., & Zolkipli, M. F. (2020). Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustainable Cities and Society, 61, 102324.
Alazab, A., Hobbs, M., Abawajy, J., & Alazab, M. (2012, October). Using feature selection for intrusion detection system. In 2012 international symposium on communications and information technologies (ISCIT) (pp. 296-301). IEEE.
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). Ieee.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
Yang, K. T. (2008). Artificial neural networks (ANNs): a new paradigm for thermal science and engineering.
Thathachar, M. A., & Sastry, P. S. (2002). Varieties of learning automata: an overview. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 32(6), 711-722.
