Abstract
One of the fast-expanding technology today is the Internet of Things (IoT). It is very necessary, to protect these machines from adversaries and unwanted entry and alteration. Intrusion Detection Systems (IDS) are techniques that can be used in information systems to monitor identified threats or anomalies. The challenge that arises is that the IDS should detect attacks on time in high-speed network traffic data. This paper proposed a modified IDS in IoT environments based on hybrid feature selection techniques for the random forest that can be used to detect intrusions with high speed and good accuracy. IoTID20 dataset is used which has three target classes which are the binary class as normal or abnormal and the classes of categories and sub-categories for the binary class. The highest-ranked attributes in the dataset are selected and the others are reduced, to minimize execution time and improve accuracy, the number of trees in the random forest classifier is reduced to 20, 25, and 20 for binary, category, and sub-category respectively. The trained classifier is then tested and achieved accuracy approaches 100% for the binary target prediction, 98.7% for category and accuracy ranges from 78.1% to 95.2% for the sub-category target prediction. The proposed system is evaluated and compared with previous ones and showed its performance.
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References
Alghuried, A.: A model for anomalies detection in internet of things (IoT) using inverse weight clustering and decision tree (2017)
Press, G.: Internet Of Things By The Numbers: What New Surveys Found (forbes.com) (2018). Accessed 1 Dec 2020
Ullah, I., Mahmoud, Q.H.: A scheme for generating a dataset for anomalous activity detection in IoT networks. In: Goutte, C., Zhu, X. (eds.) Advances in Artificial Intelligence. LNCS (LNAI), vol. 12109, pp. 508–520. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47358-7_52
Gordon, A. (ed.): Official (ISC) 2 Guide to the CISSP CBK. CRC Press, Boca Raton (2015)
Prabha, K., Sree, S.S.: A survey on IPS methods and techniques. Int. J. Comput. Sci. Issues (IJCSI) 13(2), 38 (2016)
Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2015)
Sherasiya, T., Upadhyay, H.: Intrusion detection system for Internet of Things. Int. J. Adv. Res. Innov. Ideas Educ.(IJARIIE) 2(3) (2016)
Fu, R., Zheng, K., Zhang, D., Yang, Y.: An intrusion detection scheme based on anomaly mining in Internet of Things (2011)
Liu, Y., Wu, Q.: A lightweight anomaly mining algorithm in the Internet of Things. In: IEEE 5th International Conference on Software Engineering and Service Science, pp. 1142–1145. IEEE (2014)
Hodo, E., et al.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)
Pajouh, H.H., Javidan, R., Khayami, R., Ali, D., Choo, K.K.R.: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7, 314–323 (2016)
Duda, R.O., Hart, P.E.: Pattern Classification. Wiley, Hoboken (2006)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1, no. 10. Springer, New York (2011). https://doi.org/10.1007/978-0-387-21606-5
Middleton, P., Kjeldsen, P., Tully, J.: Forecast: the Internet of Things, worldwide, Gartner Research (2013)
Jamshed, M.A., et al.: Kargus: a highly-scalable software-based intrusion detection system. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 317–328 (2012)
Jin, D., Lu, Y., Qin, J., Cheng, Z., Mao, Z.: SwiftIDS: real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism. Comput. Secur. 97, 101984 (2020)
Meesad, P., Boonrawd, P., Nuipian, V.: A chi-square-test for word importance differentiation in text classification. In: Proceedings of International Conference on Information and Electronics Engineering, pp. 110–114 (2011)
Assi, J.H., Sadiq, A.T.: NSL-KDD dataset classification using five classification methods and three feature selection strategies. J. Adv. Comput. Sci. Technol. Res. 7(1), 15–28 (2017)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE (2009)
Assi, J.H., Sadiq, A.T.: Modified artificial immune system as feature selection. Iraqi J. Sci. 733–738 (2018)
Mohsen, K.S., Sadiq, A.T.: Random forest algorithm using accuracy-based ranking. J. Comput. Theor. Nanosci. 16(3), 1039–1045 (2019)
Sadiq, A.T., Musawi, K.S.: Modify random forest algorithm using hybrid feature selection method. Int. J. Percept. Cogn. Comput. 4(2), 1–6 (2018)
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Hussein, A.Y., Falcarin, P., Sadiq, A.T. (2022). IoT Intrusion Detection Using Modified Random Forest Based on Double Feature Selection Methods. In: Liatsis, P., Hussain, A., Mostafa, S.A., Al-Jumeily, D. (eds) Emerging Technology Trends in Internet of Things and Computing. TIOTC 2021. Communications in Computer and Information Science, vol 1548. Springer, Cham. https://doi.org/10.1007/978-3-030-97255-4_5
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