An Intelligent Tree-Based Intrusion Detection Model for Cyber Security

Mohammad Al-Omari1, Majdi Rawashdeh1, Fadi Taher Qutaishat1, Mohammad Alshirah2, Nedal Ababneh3
1Department of Business Information Technology, Princess Sumaya University for Technology, Amman, Jordan
2Department of Information Systems, Al al-Bayt University, Al-Mafraq, Jordan
3Department of Information Security Engineering Technology (ISET), Abu Dhabi Polytechnic, Abu Dhabi, UAE

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Otoum, S., Kantarci, B., Mouftah, H.: A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures. arxiv.org. (2020)

Hesselman, C., Grosso, P., Holz, R., Kuipers, F., Xue, J.H., Jonker, M., de Ruiter, J., Sperotto, A., van Rijswijk-Deij, R., Moura, G.C.M., Pras, A., de Laat, C.: A responsible internet to increase trust in the digital world. J. Netw. Syst. Manag. 28, 882–922 (2020). https://doi.org/10.1007/s10922-020-09564-7

Tavallaee, M., Stakhanova, N., Ghorbani, A.A.: Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40, 516–524 (2010). https://doi.org/10.1109/TSMCC.2010.2048428

Tapiador, J.E., Orfila, A., Ribagorda, A., Ramos, B.: Key-recovery attacks on KIDS, a keyed anomaly detection system. IEEE Trans. Dependable Secur. Comput. 12, 312–325 (2015). https://doi.org/10.1109/TDSC.2013.39

Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials 18, 1153–1176 (2016). https://doi.org/10.1109/COMST.2015.2494502

Mishra, P., Varadharajan, V., Tupakula, U., Pilli, E.S.: A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutorials 21, 686–728 (2019). https://doi.org/10.1109/COMST.2018.2847722

Nisioti, A., Mylonas, A., Yoo, P.D., Katos, V.: From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods. IEEE Commun. Surv. Tutorials 20, 3369–3388 (2018). https://doi.org/10.1109/COMST.2018.2854724

Thomas, T., Vijayaraghavan, A.P., Emmanuel, S.: Machine Learning Approaches in Cyber Security Analytics. Springer, Singapore (2019)

Otoum, S., Kantarci, B., Mouftah, H.T.: A novel ensemble method for advanced intrusion detection in wireless sensor networks. In: IEEE International Conference on Communications. Institute of Electrical and Electronics Engineers Inc. (2020)

Al Ridhawi, I., Otoum, S., Aloqaily, M., Boukerche, A.: Generalizing AI: challenges and opportunities for plug and play AI solutions. IEEE Netw. (2020). https://doi.org/10.1109/MNET.011.2000371

Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020). https://doi.org/10.1016/j.jisa.2019.102419

Gumusbas, D., Yldrm, T., Genovese, A., Scotti, F.: A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems. IEEE Syst. J. (2020). https://doi.org/10.1109/jsyst.2020.2992966

Shapoorifard, H., Shamsinejad, P.: Intrusion detection using a novel hybrid method incorporating an improved KNN. Int. J. Comput. Appl. 173, 5–9 (2017). https://doi.org/10.5120/ijca2017914340

Ji, S.Y., Choi, S., Jeong, D.H.: Designing an internet traffic predictive model by applying a signal processing method. J. Netw. Syst. Manag. 23, 998–1015 (2015). https://doi.org/10.1007/s10922-014-9335-3

Ambusaidi, M.A., He, X., Nanda, P., Tan, Z.: Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 65, 2986–2998 (2016). https://doi.org/10.1109/TC.2016.2519914

Amiri, F., Rezaei Yousefi, M., Lucas, C., Shakery, A., Yazdani, N.: Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34, 1184–1199 (2011). https://doi.org/10.1016/j.jnca.2011.01.002

Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., Wang, C.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018). https://doi.org/10.1109/ACCESS.2018.2836950

Mahdavifar, S., Ghorbani, A.A.: Application of deep learning to cybersecurity: a survey. Neurocomputing 347, 149–176 (2019). https://doi.org/10.1016/j.neucom.2019.02.056

Sultana, N., Chilamkurti, N., Peng, W., Alhadad, R.: Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Netw. Appl. 12, 493–501 (2019). https://doi.org/10.1007/s12083-017-0630-0

Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS One 11, e0155781 (2016). https://doi.org/10.1371/journal.pone.0155781

Feng, F., Liu, X., Yong, B., Zhou, R., Zhou, Q.: Anomaly detection in ad-hoc networks based on deep learning model: a plug and play device. Ad Hoc Netw. 84, 82–89 (2019). https://doi.org/10.1016/j.adhoc.2018.09.014

Zhao, G., Zhang, C., Zheng, L.: Intrusion detection using deep belief network and probabilistic neural network. In: Proceedings—2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, pp. 639–642. Institute of Electrical and Electronics Engineers Inc. (2017)

Mohammadi, S., Mirvaziri, H., Ghazizadeh-Ahsaee, M., Karimipour, H.: Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019). https://doi.org/10.1016/j.jisa.2018.11.007

Aloqaily, M., Otoum, S., Al Ridhawi, I., Jararweh, Y.: An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw. 90, 101842 (2019). https://doi.org/10.1016/j.adhoc.2019.02.001

Peng, Y., Wu, Z., Jiang, J.: A novel feature selection approach for biomedical data classification. J. Biomed. Inform. 43, 15–23 (2010). https://doi.org/10.1016/j.jbi.2009.07.008

Kang, S.H., Kim, K.J.: A feature selection approach to find optimal feature subsets for the network intrusion detection system. Clust. Comput. 19, 325–333 (2016). https://doi.org/10.1007/s10586-015-0527-8

Eesa, A.S., Orman, Z., Brifcani, A.M.A.: A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst. Appl. 42, 2670–2679 (2015). https://doi.org/10.1016/j.eswa.2014.11.009

Ingre, B., Yadav, A., Soni, A.K.: Decision tree based intrusion detection system for NSL-KDD dataset. In: Satapathy S., Joshi A. (eds.) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Vol. 2, ICTIS 2017. Smart Innovation, Systems and Technologies, pp. 207–218. Springer Science and Business Media Deutschland GmbH (2018)

Moon, D., Im, H., Kim, I., Park, J.H.: DTB-IDS: an intrusion detection system based on decision tree using behavior analysis for preventing APT attacks. J. Supercomput. 73, 2881–2895 (2017). https://doi.org/10.1007/s11227-015-1604-8

Sarker, I.H., Colman, A., Han, J., Khan, A.I., Abushark, Y.B., Salah, K.: BehavDT: a behavioral decision tree learning to build user-centric context-aware predictive model. Mob. Netw. Appl. 25, 1151–1161 (2020). https://doi.org/10.1007/s11036-019-01443-z

Puthran, S., Shah, K.: Intrusion detection using improved decision tree algorithm with binary and quad split. In: Mueller P., Thampi S., Alam Bhuiyan M., Ko R., Doss R., Alcaraz Calero J. (eds.) Security in Computing and Communications, pp. 427–438. Springer (2016)

Rai, K., Syamala Devi, M., Guleria, A.: Decision tree based algorithm for intrusion detection. Int. J. Adv. Netw. Appl. 7, 2828–2834 (2016)

Sarker, I.H., Abushark, Y.B., Alsolami, F., Khan, A.I.: IntruDTree: a machine learning based cyber security intrusion detection model. Symmetry (Basel) 12, 754 (2020). https://doi.org/10.3390/SYM12050754

Kaggle, https://www.kaggle.com (2020). Accessed 24 July 2020

Zheng, A., Casari, A.: Feature Engineering for Machine Learning. O’Reilly Media, Sebastopol (2018)

Han, J., Kamber, M., Pei, J.: Data mining: Concepts and Techniques. Elsevier, Amsterdam (2012)