A Stacking-based Deep Neural Network Approach for Effective Network Anomaly Detection
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[1] 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.
[2] Bayu Adhi Tama,et al. TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System , 2019, IEEE Access.
[3] Yang-Wai Chow,et al. A Two-Stage Classifier Approach for Network Intrusion Detection , 2018, ISPEC.
[4] Bayu Adhi Tama,et al. Anomaly detection using random forest: A performance revisited , 2017, 2017 International Conference on Data and Software Engineering (ICoDSE).
[5] Bayu Adhi Tama,et al. An Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble , 2020, IEEE Access.
[6] Ernest Foo,et al. Improving performance of intrusion detection system using ensemble methods and feature selection , 2018, ACSW.
[7] Chung-Horng Lung,et al. Evaluation of machine learning techniques for network intrusion detection , 2018, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.
[8] Ludmila I. Kuncheva,et al. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.
[9] Neeraj Kumar,et al. Machine Learning Models for Secure Data Analytics: A taxonomy and threat model , 2020, Comput. Commun..
[10] K. P. Soman,et al. Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.
[11] Virender Ranga,et al. ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things , 2019, 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU).
[12] 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..
[13] Vijay Varadharajan,et al. A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection , 2019, IEEE Communications Surveys & Tutorials.
[14] Sharmila Subudhi,et al. Application of OPTICS and ensemble learning for Database Intrusion Detection , 2019, J. King Saud Univ. Comput. Inf. Sci..
[15] Leo Breiman,et al. Stacked regressions , 2004, Machine Learning.
[16] I. Sumaiya Thaseen,et al. Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers , 2018, Arabian Journal for Science and Engineering.
[17] Prabaharan Poornachandran,et al. Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
[18] Mounir Ghogho,et al. Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach , 2019, Deep Learning Applications for Cyber Security.
[19] Shadi Aljawarneh,et al. Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model , 2017, J. Comput. Sci..
[20] Ali A. Ghorbani,et al. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.
[21] Bayu Adhi Tama,et al. An in-depth experimental study of anomaly detection using gradient boosted machine , 2017, Neural Computing and Applications.
[22] Zhuo Lu,et al. Effectiveness of Machine Learning Based Intrusion Detection Systems , 2019, SpaCCS.
[23] Yuefei Zhu,et al. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.
[24] Gholamhossein Dastghaibyfard,et al. Two-tier network anomaly detection model: a machine learning approach , 2017, Journal of Intelligent Information Systems.
[25] Jiankun Hu,et al. A holistic review of Network Anomaly Detection Systems: A comprehensive survey , 2019, J. Netw. Comput. Appl..
[26] Abdallah Shami,et al. Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).
[27] Omar Y. Al-Jarrah,et al. Semi-supervised multi-layered clustering model for intrusion detection , 2017, Digit. Commun. Networks.
[28] Seema Shah,et al. A Comprehensive Survey of Machine Learning-Based Network Intrusion Detection , 2018, Smart Intelligent Computing and Applications.
[29] Iraj Mahdavi,et al. Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms , 2019, J. King Saud Univ. Comput. Inf. Sci..
[30] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[31] João Paulo Papa,et al. Internet of Things: A survey on machine learning-based intrusion detection approaches , 2019, Comput. Networks.
[32] Iqbal Gondal,et al. Survey of intrusion detection systems: techniques, datasets and challenges , 2019, Cybersecurity.
[33] Salah El Hadaj,et al. Performance evaluation of intrusion detection based on machine learning using Apache Spark , 2018 .
[34] Iftikhar Ahmad,et al. Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000 , 2018 .