Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
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[1] David Cortes,et al. Revisiting randomized choices in isolation forests , 2021, ArXiv.
[2] Fuad A. Ghaleb,et al. Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review , 2021, Applied Sciences.
[3] Dohyeun Kim,et al. An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments , 2021, Sustainability.
[4] Rosilah Hassan,et al. Anomaly Detection Using Deep Neural Network for IoT Architecture , 2021, Applied Sciences.
[5] Chin-Wei Tien,et al. Using Autoencoders for Anomaly Detection and Transfer Learning in IoT , 2021, Comput..
[6] Mohamed Abdel-Basset,et al. Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks , 2021, IEEE Internet of Things Journal.
[7] Yan Xu,et al. Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication , 2021, IEEE Internet of Things Journal.
[8] Robert J. Brunner,et al. Extended Isolation Forest , 2018, IEEE Transactions on Knowledge and Data Engineering.
[9] Giuseppe Pirlo,et al. Ensemble Consensus: An Unsupervised Algorithm for Anomaly Detection in Network Security data , 2021, ITASEC.
[10] Isabel Praça,et al. Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems , 2020, Applied Sciences.
[11] Eryk Dutkiewicz,et al. Deep Transfer Learning for IoT Attack Detection , 2020, IEEE Access.
[12] Javier Bermejo Higuera,et al. Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review , 2020, Sustainability.
[13] Yingying Xu,et al. Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning , 2019, IFTC.
[14] Svetha Venkatesh,et al. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[15] Ridwan Nur Wibowo,et al. NSL-KDD Dataset , 2019 .
[16] John Yen,et al. Using Bayesian Networks for Probabilistic Identification of Zero-Day Attack Paths , 2018, IEEE Transactions on Information Forensics and Security.
[17] Yuval Elovici,et al. Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection , 2018, NDSS.
[18] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[19] Samarjeet Borah,et al. A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems , 2018 .
[20] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[21] Zhaohui Wu,et al. Discovering different kinds of smartphone users through their application usage behaviors , 2016, UbiComp.
[22] Yu Cheng,et al. Deep Structured Energy Based Models for Anomaly Detection , 2016, ICML.
[23] S. P. Shantharajah,et al. A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms , 2015 .
[24] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[25] José Antonio Lozano,et al. Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] 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.
[27] Zhi-Hua Zhou,et al. Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[28] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[29] Wenjie Hu,et al. Robust Anomaly Detection Using Support Vector Machines , 2003 .
[30] Guoqiang Peter Zhang,et al. Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.
[31] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD '00.
[32] Katrien van Driessen,et al. A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.