Abstract
In the past few years, numerous machine learning techniques have been employed in IoT networks to develop Intrusion Detection Systems (IDS) that differentiate abnormal activities caused by malicious intruders from the typical network behavior. Due to the large volume of data produced by IoT devices, it is challenging to perform real-time classification of data to find any abnormal patterns. Single classifier-based approaches are considered to be simple and straightforward but may not be able to capture all the relevant information in the data, leading to suboptimal performance. To overcome the weaknesses of single classifiers, it is often beneficial to use an ensemble of classifiers, such as a random forest or a gradient-boosted trees model, which can capture a wider range of patterns in the data and lead to improved performance. However, ensemble models can be computationally expensive to train especially when using large numbers of base classifiers making it difficult to scale the models to large datasets, such as of IoT. This paper presents a detailed empirical analysis of the comparative performance of single classifier versus ensemble models for intrusion detection in IoT networks by utilizing two benchmark datasets in the Internet of Things: NSL-KDD and UNSW-NB15. It has been observed that under certain conditions, the performance of single classifier-based IDS surpasses the ensemble stacking approaches. Moreover, training/testing dataset selection has a major impact on overall validation and testing performance of the models. Based on the empirical observations, we use a novel method known as ensemble stacking approach that outperforms the baselines for the selected datasets. The research provides a detailed insight into the impact of various classifiers and dataset features on the performance of IDS in IoT environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atzoria, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Kardi, A., Zagrouba, R.: Attacks classification and security mechanisms in wireless sensor networks. Adv. Sci. Technol. Eng. Syst. J. 4(6), 229–243 (2019)
Jha, S., Nkenyereye, L., Joshi, G.P., Yang, E.: Mitigating and monitoring smart city using internet of things. Comput. Mater. Continua 65(2), 1059–1079 (2020)
Abbas, S., Khan, M.A., Falcon Morales, L.E., Rehman, A., Mahmoud, M.E., Zeb, A.: Modelling, simulation and optimization of power plan energy sustainability for IoT enabled smart cities empowered with deep extreme leaning machine. IEEE Access 8(1), 39982–39997 (2020)
Alhajri, R., Zagrouba, R., Al-Haidari, F.: Survey for anomaly detection of IoT botnets using machine learning auto-encoders. Int. J. Appl. Eng. Res. 14(1), 2417–2421 (2019)
Ahmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Emerg. Telecommun. Technol. 3(1), 70-99 (2020)
Javaid, U., Siang, A.K., Aman, M.N., Sikdar, B.: Mitigating loT device based DDoS attacks using blockchain. In: Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, vol. 1, no. 2, pp. 71–76 (2018)
Ting, P.Y., Tsai, J.L., Wu, T.S.: Signcryption method suitable for low-power IoT devices in a wireless sensor network. IEEE Syst. J. 12(3), 2385–2394 (2018)
Moinet, A., Darties, B., Baril, J.L.: Blockchain based trust and authentication for decentralized sensor networks. Comput. Sci. Cryptogr. Secur. 1(1), 1–6 (2017)
Rashid, M., Kamruzzaman, J., Imam, T., Wibowo, S., Gordon, S.: A tree-based stacking ensemble technique with feature selection for network intrusion detection. Appl. Intell. 1(2), 9768–9781 (2022)
Bamhdi, A.M., Abrar, I., Masoodi, F.: An ensemble based approach for effective intrusion detection using majority voting. Telecommun. Comput. Electr. Control 19(2), 1–15 (2021)
Rajagopal, S., Kundapur, P.P., Hareesha, K.S.: A stacking ensemble for network intrusion detection using heterogeneous datasets. Secur. Commun. Netw. 2020(1), 1–9 (2020)
Canadian Institute for Cybersecurity: NSL-KDD dataset [Online]. Available: https://www.unb.ca/cic/datasets/nsl.html. Accessed 24 Feb 2023
The UNSW-NB15 Dataset. [Online]. Available: https://research.unsw.edu.au/projects/unsw-nb15-dataset. Accessed 24 Feb. 2023
Zhang, H., Li, J.L., Liu, X.M., Dong, C.: Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection. Futur. Gener. Comput. Syst. 122(1), 130–143 (2021)
Dutta, V., Choraś, M., Pawlicki, M., Kozik, R.: A deep learning ensemble for network anomaly and cyber-attack detection. MDPI 20(16), 1–15 (2020)
Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A.: Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine. Electronics 9(1), 1–15 (2020)
Soleymanzadeh, R., Aljasim, M., Qadeer, M.W.: Cyberattack and fraud detection using ensemble stacking. Artif. Intell. 3(1), 22–36 (2022)
Rahman, M.A., Asyhari, A.T., Wen, O.W., Ajra, H., Ahmed, Y., Anwar, F.: Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimed. Tools Appl. 80(20), 31381–31399 (2021). https://doi.org/10.1007/s11042-021-10567-y
Kumar, P., Gupta, G.P., Tripathi, R.: A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks. J. Ambient. Intell. Humaniz. Comput. 12(1), 9555–9572 (2020). https://doi.org/10.1007/s12652-020-02696-3
Abdulrahaman, M. D., Alhassan, J.K.: Ensemble learning approach for the enhancement of performance of intrusion detection system. In: International Conference on Information and Communication Technology and its Application, vol. 2, no. 1, pp. 1–14 (2018)
Illy, P., Kaddoum, G., Mirand, C.: Securing fog-to-things environment using intrusion detection system based on ensemble learning. In: IEEE Wireless Communications and Networking Conference (WCNC), vol. 12, no. 4, pp. 1–7 (2019)
Verma, A., Ranga, V.: Machine learning based intrusion detection systems for IoT applications. Wirel. Pers. Commun. 16(3), 2287–2310 (2019). https://doi.org/10.1007/s11277-019-06986-8
Li, X., et al.: Sustainable ensemble learning driving intrusion detection model. IEEE Trans. Dependable Secure Comput. 18(4), 1591–1604 (2021)
Attota, D.C., Mothukuri, V., Parizi, R.M., Pouriyeh, S.: An ensemble multi-view federated learning intrusion detection for IoT. IEEE Access 9(3), 117734–117745 (2021)
Abbas, A., Khan, M.A., Latif, S., Ajaz, M., Shah, A.A., Ahmad, J.: A new ensemble-based intrusion detection system for internet of things. Arab. J. Sci. Eng. 47, 1–15 (2021). https://doi.org/10.1007/s13369-021-06086-5
Ahmad, M., Riaz, Q., Zeeshan, M., Tahir, H., Haider, S.A., Khan, M.S.: Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set. Wirel. Commun. Netw. 10(1), 1–23 (2021). https://doi.org/10.1186/s13638-021-01893-8
Yin, Y., Jaccard, J.J., Singh, A., Zhu, J., Sabrina, F., Kwak, J.: IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset. J. Big Data 10(2), 1–26 (2023)
Gad, A.R., Nashat, A.A., Barkat, T.A.: Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access 9(3), 1–12 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rais, R.N.B., Khalid, O., Nazar, Je., Khan, M.U.S. (2023). Analysis of Intrusion Detection Using Ensemble Stacking-Based Machine Learning Techniques in IoT Networks. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-33743-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33742-0
Online ISBN: 978-3-031-33743-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)