A Reliable Network Intrusion Detection Approach Using Decision Tree with Enhanced Data Quality

Due to the recent advancements in the Internet of things (IoT) and cloud computing technologies and growing number of devices connected to the Internet, the security and privacy issues are important to be resolved and protect the data and computer network. To provide security, a real-time monitoring of the network data and resources is needed. Intrusion detection systems have been used to monitor, detect, and alert an intrusion event in real time. Recently, the intrusion detection systems (IDS) incorporate several machine learning (ML) techniques. One of the techniques is decision tree, which can take reliable network measures and make good decisions by increasing the detection rate and accuracy. In this paper, we propose a reliable network intrusion detection approach using decision tree with enhanced data quality. Specifically, network data preprocessing and entropy decision feature selection is carried out for enhancing the data quality and relevant training; then, a decision tree classifier is built for reliable intrusion detection. Experimental study on two datasets shows that the proposed model can reach robust results. Actually, our model achieves 99.42% and 98.80% accuracy with NSL-KDD and CICIDS2017 datasets, respectively. The novel approach gives many advantages compared to the other models in term of accuracy (ACC), detection rate (DR), and false alarm rate (FAR).

[1]  Sachin Tripathi,et al.  An efficient feature selection based Bayesian and Rough set approach for intrusion detection , 2020, Appl. Soft Comput..

[2]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[3]  Esmaeil Hadavandi,et al.  A Neural Network Ensemble Classifier for Effective Intrusion Detection Using Fuzzy Clustering and Radial Basis Function Networks , 2016, Int. J. Artif. Intell. Tools.

[4]  Gabriela Grosseck,et al.  Knowledge Based and Intelligent Information and Engineering Systems , KES 2017 , 6-8 September 2017 , Marseilles , France Decision tree learning used for the classification of student archetypes in online courses , 2017 .

[5]  Ahmad Sharieh,et al.  A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer , 2020 .

[6]  Yassine Sadqi,et al.  A Global Intrusion Detection System using PcapSockS Sniffer and Multilayer Perceptron Classifier , 2019, Int. J. Netw. Secur..

[7]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[8]  Neeraj Kumar,et al.  A Novel Pairing-Free Lightweight Authentication Protocol for Mobile Cloud Computing Framework , 2021, IEEE Systems Journal.

[9]  Akhan Akbulut,et al.  Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic , 2020, Comput. Networks.

[10]  Xiangjian He,et al.  Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm , 2016, IEEE Transactions on Computers.

[11]  Seungmin Rho,et al.  An improved anonymous authentication scheme for distributed mobile cloud computing services , 2017, Cluster Computing.

[12]  Shoushan Luo,et al.  A two-level hybrid approach for intrusion detection , 2016, Neurocomputing.

[13]  W. Marsden.  I and J , 2012 .

[14]  Jasmin Kevric,et al.  An effective combining classifier approach using tree algorithms for network intrusion detection , 2017, Neural Computing and Applications.

[15]  Mohamed Amine Ferrag,et al.  Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study , 2020, J. Inf. Secur. Appl..

[16]  Rahul Kumar,et al.  A context-aware robust intrusion detection system: a reinforcement learning-based approach , 2019, International Journal of Information Security.

[17]  Mohamed Rida,et al.  Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms , 2019, Comput. Secur..

[18]  Iqbal Gondal,et al.  Survey of intrusion detection systems: techniques, datasets and challenges , 2019, Cybersecurity.

[19]  Joel J. P. C. Rodrigues,et al.  A comprehensive survey on network anomaly detection , 2018, Telecommunication Systems.

[20]  Vern Paxson,et al.  Outside the Closed World: On Using Machine Learning for Network Intrusion Detection , 2010, 2010 IEEE Symposium on Security and Privacy.

[21]  Giancarlo Fortino,et al.  A hybrid deep learning model for efficient intrusion detection in big data environment , 2020, Inf. Sci..

[22]  Ünal Çavusoglu,et al.  A new hybrid approach for intrusion detection using machine learning methods , 2019, Applied Intelligence.

[23]  Ali A. Ghorbani,et al.  Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.

[24]  Jie Gu,et al.  A novel approach to intrusion detection using SVM ensemble with feature augmentation , 2019, Comput. Secur..

[25]  Mehrdad Rostami,et al.  Review of Swarm Intelligence-based Feature Selection Methods , 2020, Eng. Appl. Artif. Intell..

[26]  Muhammad Usman,et al.  A Provably Secure and Efficient Authenticated Key Agreement Scheme for Energy Internet-Based Vehicle-to-Grid Technology Framework , 2020, IEEE Transactions on Industry Applications.

[27]  Mohammed Tabash,et al.  Intrusion detection model using naive bayes and deep learning technique , 2020, Int. Arab J. Inf. Technol..

[28]  Femi Emmanuel Ayo,et al.  Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection , 2020, Inf. Secur. J. A Glob. Perspect..

[29]  Al-Furat Al-Awsat.  Performance Analysis of Big Data Intrusion Detection System over Random Forest Algorithm , 2018 .

[30]  Kaliappan Jeyakumar,et al.  Intrusion detection using artificial neural networks with best set of features , 2015, Int. Arab J. Inf. Technol..

[31]  Ahmed Ahmim,et al.  A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models , 2018, 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).

[32]  Mohammad Masdari,et al.  A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems , 2020, Appl. Soft Comput..

[33]  Dong Hyun Jeong,et al.  A multi-level intrusion detection method for abnormal network behaviors , 2016, J. Netw. Comput. Appl..

[34]  Amin Karami,et al.  An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities , 2018, Expert Syst. Appl..

[35]  Abdelouahid Derhab,et al.  Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues , 2020, Knowl. Based Syst..

[36]  Ahmed Asimi,et al.  Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection , 2021, Big Data Min. Anal..

[37]  Shehzad Ashraf Chaudhry,et al.  PFLUA-DIoT: A Pairing Free Lightweight and Unlinkable User Access Control Scheme for Distributed IoT Environments , 2020, IEEE Systems Journal.

[38]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[39]  Jill Slay,et al.  The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set , 2016, Inf. Secur. J. A Glob. Perspect..

[40]  Weijian Fang,et al.  Application of intrusion detection technology in network safety based on machine learning , 2020 .