Abstract
Intrusion detection in Internet of Things (IoT) networks is a critical challenge due to the dynamic and evolving nature of cyber threats. To address this issue, we propose a novel hybrid intrusion detection algorithm that combines the deep autoencoder-based intrusion detection system (DAIDS) with recurrent neural networks (RNNs). The hybrid model aims to leverage the strengths of both approaches to enhance the accuracy and robustness of intrusion detection in IoT networks. The DAIDS component employs a deep autoencoder to capture complex spatial patterns in the network traffic data and calculate the reconstruction error as an anomaly score. The RNN component focuses on capturing temporal dependencies within sequential traffic data, providing a comprehensive understanding of network behavior over time. The output of the RNN is integrated into the DAIDS framework, augmenting the feature representation and enabling better anomaly detection. Experimental evaluation is conducted on a diverse IoT network traffic dataset, and the results showcase the effectiveness of the proposed hybrid DAIDS-RNN model. Comparative analysis demonstrates that the hybrid approach outperforms stand-alone DAIDS and RNN models in terms of accuracy, precision, recall, and F1-score. The hybrid model’s ability to identify both known and evolving attack patterns highlights its potential to enhance IoT intrusion detection systems. This study contributes to the advancement of intrusion detection techniques for IoT networks by introducing a hybrid approach that marries spatial and temporal insights for improved detection accuracy and adaptability to evolving threats. The proposed hybrid DAIDS-RNN model demonstrates its applicability in real-world IoT security scenarios, paving the way for more effective with 99.64 % accuracy and robust intrusion detection systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aboelwafa, M.M.N., Seddik, K.G., Eldefrawy, M.H., Gadallah, Y., Gidlund, M.: A machine-learning-based technique for false data injection attacks detection in industrial IoT. IEEE Internet Things J. 7(9), 8462–8471 (2020). https://doi.org/10.1109/JIOT.2020.2991693
Ahmed, M., Mahmood, A.N., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)
Ahmed, S.W., Kientz, F., Kashef, R.: A modified transformer neural network (MTNN) for robust intrusion detection in IoT networks. In: 2023 International Telecommunications Conference (ITC-Egypt), pp. 663–668 (2023). https://doi.org/10.1109/ITC-Egypt58155.2023.10206134
Alkahtani, H., Aldhyani, T.H.: Intrusion detection system to advance Internet of Things infrastructure-based deep learning algorithms. Complexity 2021 (2021)
Alqarni, M.: IoT intrusion detection dataset (2022). https://www.kaggle.com/datasets/malqarni/iotdataset
Alqarni, M., Azim, A.: Software source code vulnerability detection using advanced deep convolutional neural network. In: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp. 226–231 (2021)
Bach, M., Werner, A., Palt, M.: The proposal of undersampling method for learning from imbalanced datasets. Proc. Comput. Sci. 159, 125–134 (2019)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2) (2012)
Boumkheld, N., Ghogho, M., El Koutbi, M.: Intrusion detection system for the detection of blackhole attacks in a smart grid. In: 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), pp. 108–111. IEEE, Piscataway (2016)
Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: 2010 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE, Piscataway (2010)
Cleetus, N., Dhanya, K.: Multi-objective functions in particle swarm optimization for intrusion detection. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 387–392. IEEE, Piscataway (2014)
da Costa, K.A., Papa, J.P., Lisboa, C.O., Munoz, R., de Albuquerque, V.H.C.: Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147–157 (2019). https://doi.org/https://doi.org/10.1016/j.comnet.2019.01.023. https://www.sciencedirect.com/science/article/pii/S1389128618308739
Djellali, C., Adda, M.: A new hybrid deep learning model based-recommender system using artificial neural network and hidden Markov model. Procedia Computer Science 175, 214–220 (2020). https://doi.org/https://doi.org/10.1016/j.procs.2020.07.032. https://www.sciencedirect.com/science/article/pii/S1877050920317129. The 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC),The 15th International Conference on Future Networks and Communications (FNC),The 10th International Conference on Sustainable Energy Information Technology
Enache, A.C., Sgârciu, V.: A feature selection approach implemented with the binary bat algorithm applied for intrusion detection. In: 2015 38th International Conference on Telecommunications and Signal Processing (TSP), pp. 11–15 (2015). https://doi.org/10.1109/TSP.2015.7296215
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009)
Grzymala-Busse, J.W., Stefanowski, J., Wilk, S.: A comparison of two approaches to data mining from imbalanced data. J. Intell. Manuf. 16(6), 565–573 (2005)
Hanif, S., Ilyas, T., Zeeshan, M.: Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset. In: 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), pp. 152–156. IEEE, Piscataway (2019)
Hasan, M.N., Toma, R.N., Nahid, A.A., Islam, M.M., Kim, J.M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)
Hashish, I.A., Forni, F., Andreotti, G., Facchinetti, T., Darjani, S.: A hybrid model for bitcoin prices prediction using hidden Markov models and optimized LSTM networks. In: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 721–728 (2019). https://doi.org/10.1109/ETFA.2019.8869094
Iasiello, E.: Cyber attack: A dull tool to shape foreign policy. In: 2013 5th International Conference on Cyber Conflict (CYCON 2013), pp. 1–18. IEEE, Piscataway (2013)
Jokar, P., Leung, V.C.: Intrusion detection and prevention for ZigBee-based home area networks in smart grids. IEEE Trans. Smart Grid 9(3), 1800–1811 (2016)
Kareem, S., SS, K., Mostafa, R., RR, M.: An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection, vol. 2022. Basel (2022)
Khan, M.N., Rao, A., Camtepe, S.: Lightweight cryptographic protocols for IoT-constrained devices: a survey. IEEE Internet Things J. 8(6), 4132–4156 (2021). https://doi.org/10.1109/JIOT.2020.3026493
Kiran, K.S., Devisetty, R.K., Kalyan, N.P., Mukundini, K., Karthi, R.: Building a intrusion detection system for IoT environment using machine learning techniques. Proc. Comput. Sci. 171, 2372–2379 (2020)
Li, W., Tug, S., Meng, W., Wang, Y.: Designing collaborative blockchained signature-based intrusion detection in IoT environments. Fut. Gener. Comput. Syst. 96, 481–489 (2019). https://doi.org/https://doi.org/10.1016/j.future.2019.02.064. https://www.sciencedirect.com/science/article/pii/S0167739X18327237
Lin, X.X., Lin, P., Yeh, E.H.: Anomaly detection/prediction for the internet of things: state of the art and the future. IEEE Netw. 35(1), 212–218 (2021). https://doi.org/10.1109/MNET.001.1800552
Martins, A., Mateus, B., Fonseca, I., Farinha, J.T., Rodrigues, J., Mendes, M., Cardoso, A.M.: Predicting the health status of a pulp press based on deep neural networks and hidden Markov models. Energies 16(6) (2023). https://doi.org/10.3390/en16062651. https://www.mdpi.com/1996-1073/16/6/2651
Mohammed, A., Saleh, A., Majdi, B., Muder, A., Salwani, A.: Intrusion detection for IoT based on a hybrid shuffled shepherd optimization algorithm. In: The Journal of Supercomputing. The Journal of Supercomputing (2022)
Muhammad, K., Hussain, T., Tanveer, M., Sannino, G., de Albuquerque, V.H.C.: Cost-effective video summarization using deep CNN with hierarchical weighted fusion for IoT surveillance networks. IEEE Internet Things J. 7(5), 4455–4463 (2020). https://doi.org/10.1109/JIOT.2019.2950469
Saeed, A., Ahmadinia, A., Javed, A., Larijani, H.: Intelligent intrusion detection in low power IoTs. ACM Trans. Internet Technol. 16(4), 1–25 (2016)
Saxena, H., Richariya, V.: Intrusion detection in kdd99 dataset using SVM-PSO and feature reduction with information gain. Int. J. Comput. Appl. 98(6) (2014)
Sebastian Garcia Agustin Parmisano, M.J.E.: Iot-23: A labeled dataset with malicious and benign IoT network traffic (version 1.0.0) [data set]. Zenodo (2020). http://doi.org/10.5281/zenodo.4743746
da Silva, A.S., Wickboldt, J.A., Granville, L.Z., Schaeffer-Filho, A.: Atlantic: a framework for anomaly traffic detection, classification, and mitigation in SDN. In: NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp. 27–35. IEEE, Piscataway (2016)
Suresh, G.M., Madhavu, M.L.: Ai based intrusion detection system using self-adaptive energy efficient bat algorithm for software defined IoT networks. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE, Piscataway (2020)
Tahsien, S.M., Karimipour, H., Spachos, P.: Machine learning based solutions for security of internet of things (Iot): a survey. J. Netw. Comput. Appl. 161, 102630 (2020). https://doi.org/https://doi.org/10.1016/j.jnca.2020.102630. https://www.sciencedirect.com/science/article/pii/S1084804520301041
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: 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 (2009). https://doi.org/10.1109/CISDA.2009.5356528
Thant, Y.M., Su Thwin, M.M., Htwe, C.S.: Iot network intrusion detection using long short-term memory recurrent neural network. In: 2023 IEEE Conference on Computer Applications (ICCA), pp. 334–339 (2023). https://doi.org/10.1109/ICCA51723.2023.10182005
Ullah, I., Mahmoud, Q.H.: A scheme for generating a dataset for anomalous activity detection in IoT networks. In: Canadian Conference on Artificial Intelligence, pp. 508–520. Springer, Berlin (2020)
Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., Zhu, M.: HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792–1806 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Alqarni, M., Azim, A. (2024). Enhancing Embedded IoT Systems for Intrusion Detection Using a Hybrid Model. In: Sipola, T., Alatalo, J., Wolfmayr, M., Kokkonen, T. (eds) Artificial Intelligence for Security. Springer, Cham. https://doi.org/10.1007/978-3-031-57452-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-57452-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57451-1
Online ISBN: 978-3-031-57452-8
eBook Packages: Computer ScienceComputer Science (R0)