Intrusion Detection System Development on Internet of Things using Ensemble Learning
Main Article Content
Keywords
IDS, IOT, machine learning, security
Abstract
The utilization of intrusion detection systems (IDS) can significantly enhance the security of IT infrastructure. Machine learning (ML) methods have emerged as a promising approach to improving the capabilities of IDS. The primary objective of an IDS is to detect various types of malicious intrusions with a high detection rate while minimizing false alarms, surpassing the capabilities of a firewall. However, developing an IDS for IOT poses substantial challenges due to the massive volume of data that needs to be processed. To address this, an optimal approach is required to improve the accuracy of data containing numerous attacks. In this study, we propose a novel IDS model that employs the Random Forest, Decision Tree, and Logistic Regression algorithms using a specialized ML technique known as Ensemble Learning. For this research, we used the BoT-IoT datasets as inputs for the IDS model to distinguish between malicious and benign network traffic. To determine the best model, we compared the performance metrics of each algorithm across different parameter combinations. The research findings demonstrate exceptional performance, with metric scores exceeding 99.995% for all parameter combinations. Based on these conclusive results, we deduce that the proposed model achieves remarkable success and outperforms other traditional ML-based IDS models in terms of performance metrics. These outcomes highlight the potential of our novel IDS model to enhance the security posture of IoT-based systems significantly.
References
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[2] K. Albulayhi, Q. Abu Al-Haija, S. A. Alsuhibany, A. A. Jillepalli, M. Ashrafuzzaman, and F. T. Sheldon, “IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method,” Applied Sciences, vol. 12, no. 10, 2022, doi: 10.3390/app12105015.
[3] N. Koroniotis, N. Moustafa, E. Sitnikova, and J. Slay, “Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques,” Jun. 2017.
[4] H. A. Khattak, M. A. Shah, S. Khan, I. Ali, and M. Imran, “Perception layer security in Internet of Things,” Future Generation Computer Systems, vol. 100, pp. 144–164, Nov. 2019, doi: 10.1016/J.FUTURE.2019.04.038.
[5] T. Saba, T. Sadad, A. Rehman, Z. Mehmood, and Q. Javaid, “Intrusion Detection System Through Advance Machine Learning for the Internet of Things Networks,” IT Prof, vol. 23, no. 2, pp. 58–64, 2021, doi: 10.1109/MITP.2020.2992710.
[6] A. Khraisat, I. Gondal, P. Vamplew, J. Kamruzzaman, and A. Alazab, “A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks,” Electronics (Switzerland), vol. 8, no. 11, 2019, doi: 10.3390/electronics8111210.
[7] N. Koroniotis, N. Moustafa, F. Schiliro, P. Gauravaram, and H. Janicke, “A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports,” IEEE Access, vol. 8, pp. 209802–209834, 2020, doi: 10.1109/ACCESS.2020.3036728.
[8] N. Koroniotis, N. Moustafa, and E. Sitnikova, “A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework,” Future Generation Computer Systems, vol. 110, pp. 91–106, 2020, doi: https://doi.org/10.1016/j.future.2020.03.042.
[9] N. Koroniotis and N. Moustafa, “Enhancing network forensics with particle swarm and deep learning: The particle deep framework,” CoRR, vol. abs/2005.00722, 2020, [Online]. Available: https://arxiv.org/abs/2005.00722
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[12] A. Abbas, M. A. Khan, S. Latif, M. Ajaz, A. A. Shah, and J. Ahmad, “A New Ensemble-Based Intrusion Detection System for Internet of Things,” Arab J Sci Eng, vol. 47, no. 2, pp. 1805–1819, 2022, doi: 10.1007/s13369-021-06086-5.
[13] S. Alshathri, A. El-Sayed, W. El-Shafai, and E. El-Din Hemdan, “An Efficient Intrusion Detection Framework for Industrial Internet of Things Security,” Computer Systems Science and Engineering, vol. 46, no. 1, pp. 819–834, 2023, doi: 10.32604/csse.2023.034095.
[14] A. Aljuhani, “Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments,” IEEE Access, vol. 9, pp. 42236–42264, 2021, doi: 10.1109/ACCESS.2021.3062909.
[15] F. Liang, W. G. Hatcher, W. Liao, W. Gao, and W. Yu, “Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly,” IEEE Access, vol. 7, pp. 158126–158147, 2019, doi: 10.1109/ACCESS.2019.2948912.
[16] R. Abu Bakar, X. Huang, M. S. Javed, S. Hussain, and M. F. Majeed, “An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection,” Sensors, vol. 23, no. 6, 2023, doi: 10.3390/s23063333.
[17] R. Alanazi and A. Aljuhani, “Anomaly Detection for Industrial Internet of Things Cyberattacks,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 2361–2378, 2023, doi: 10.32604/csse.2023.026712.
[18] Q. A. Al-Haija and M. Al-Dala’ien, “ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks,” Journal of Sensor and Actuator Networks, vol. 11, no. 1, 2022, doi: 10.3390/jsan11010018.
[19] N. Koroniotis, “Designing an effective network forensic framework for the investigation of botnets in the Internet of Things,” 2020.
[20] A. Koirala, R. Bista, and J. C. Ferreira, “Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms,” Future Internet, vol. 15, no. 6, 2023, doi: 10.3390/fi15060210.
[21] N. Naz et al., “Ensemble learning-based IDS for sensors telemetry data in IoT networks,” Mathematical Biosciences and Engineering, vol. 19, no. 10, pp. 10550 – 10580, 2022, doi: 10.3934/mbe.2022493.
[22] Y. Alotaibi and M. Ilyas, “Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security,” Sensors, vol. 23, no. 12, 2023, doi: 10.3390/s23125568.
[23] J. B. Awotunde, F. E. Ayo, R. Panigrahi, A. Garg, A. K. Bhoi, and P. Barsocchi, “A Multi-level Random Forest Model-Based Intrusion Detection Using Fuzzy Inference System for Internet of Things Networks,” International Journal of Computational Intelligence Systems, vol. 16, no. 1, 2023, doi: 10.1007/s44196-023-00205-w.
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[25] M. and O. S. and A. F. Alshamy Reem and Ghurab, “Intrusion Detection Model for Imbalanced Dataset Using SMOTE and Random Forest Algorithm,” in Advances in Cyber Security, S. and A. M. Abdullah Nibras and Manickam, Ed., Singapore: Springer Singapore, 2021, pp. 361–378.
[26] E. Alshahrani, D. Alghazzawi, R. Alotaibi, and O. Rabie, “Adversarial attacks against supervised machine learning based network intrusion detection systems,” PLoS One, vol. 17, no. 10 October, 2022, doi: 10.1371/journal.pone.0275971.
[27] M. Bhati Nitesh Singh and Khari, “An Ensemble Model for Network Intrusion Detection Using AdaBoost, Random Forest and Logistic Regression,” in Applications of Artificial Intelligence and Machine Learning, H. M. and R. G. Unhelker Bhuvan and Pandey, Ed., Singapore: Springer Nature Singapore, 2022, pp. 777–789.
[28] A. Churcher et al., “An experimental analysis of attack classification using machine learning in IoT networks,” Sensors (Switzerland), vol. 21, no. 2, pp. 1–32, 2021, doi: 10.3390/s21020446.
[29] P. Ghosh and R. Mitra, “Proposed GA-BFSS and logistic regression based intrusion detection system,” Jun. 2015, pp. 1–6. doi: 10.1109/C3IT.2015.7060117.
[30] A. Koirala, R. Bista, and J. C. Ferreira, “Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms,” Future Internet, vol. 15, no. 6, 2023, doi: 10.3390/fi15060210.
[31] A. Al-Saleh, “A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems,” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-36304-z.
[32] Y. Saheed, O. Abdulganiyu, and T. Ait Tchakoucht, “A Novel Hybrid Ensemble Learning for Anomaly Detection in Industrial Sensor Networks and SCADA Systems for Smart City Infrastructures,” Journal of King Saud University - Computer and Information Sciences, vol. 35, Jun. 2023, doi: 10.1016/j.jksuci.2023.03.010.
[33] B. I. Hairab, M. Said Elsayed, A. D. Jurcut, and M. A. Azer, “Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks,” IEEE Access, vol. 10, pp. 98427–98440, 2022, doi: 10.1109/ACCESS.2022.3206367.
[34] O. D. and A. A. O. and M. J. O. Mebawondu Olamatanmi J. and Alowolodu, “Optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm,” in Information and Communication Technology and Applications, B. Misra Sanjay and Muhammad-Bello, Ed., Cham: Springer International Publishing, 2021, pp. 286–300.
[35] N. Abdullah, S. Manickam, and M. Anbar, Advances in Cyber Security Third International Conference, ACeS 2021, Penang, Malaysia, August 24–25, 2021, Revised Selected Papers: Third International Conference, ACeS 2021, Penang, Malaysia, August 24–25, 2021, Revised Selected Papers. 2021. doi: 10.1007/978-981-16-8059-5.
[36] D. Rani, N. S. Gill, P. Gulia, F. Arena, and G. Pau, “Design of an Intrusion Detection Model for IoT-Enabled Smart Home,” IEEE Access, vol. 11, pp. 52509–52526, 2023, doi: 10.1109/ACCESS.2023.3276863.
[37] S. Rachmadi, S. Mandala, and D. Oktaria, “Detection of DoS Attack using AdaBoost Algorithm on IoT System,” in 2021 International Conference on Data Science and Its Applications (ICoDSA), 2021, pp. 28–33. doi: 10.1109/ICoDSA53588.2021.9617545.
[38] N. Meemongkolkiat and V. Suttichaya, “Analysis on Network Traffic Features for Designing Machine Learning based IDS,” in Journal of Physics: Conference Series, 2021. doi: 10.1088/1742-6596/1993/1/012029.
[39] B. Kerim, “Securing IoT Network against DDoS Attacks using Multi-agent IDS,” in Journal of Physics: Conference Series, 2021. doi: 10.1088/1742-6596/1898/1/012033.
[40] T. Ghrib, M. Benmohammed, and P. S. Pandey, “Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 950–961, 2021, doi: 10.11591/eei.v10i2.2766.
[41] S. Seth, K. K. Chahal, and G. Singh, “A Novel Ensemble Framework for an Intelligent Intrusion Detection System,” IEEE Access, vol. 9, pp. 138451–138467, 2021, doi: 10.1109/ACCESS.2021.3116219.
[42] A. Mahfouz, A. Abuhussein, D. Venugopal, and S. Shiva, “Ensemble classifiers for network intrusion detection using a novel network attack dataset,” Future Internet, vol. 12, no. 11, pp. 1–19, 2020, doi: 10.3390/fi12110180.
[43] W. Lian, G. Nie, B. Jia, D. Shi, Q. Fan, and Y. Liang, “An intrusion detection method based on decision tree-recursive feature elimination in ensemble learning,” Math Probl Eng, vol. 2020, 2020, doi: 10.1155/2020/2835023.
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Jul 31, 2024
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Nadia Ariana, Satria Mandala, Mohd Fadzil Hasssan, Muhammad Qomaruddin, & Bilal Ibrahim Bakri. (2024). Intrusion Detection System Development on Internet of Things using Ensemble Learning. Jurnal Nasional Teknik Elektro, 13(2), 75–81. https://doi.org/10.25077/jnte.v13n2.1113.2024
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Telecommunication
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