Gated recurrent unit-based parallel network traffic anomaly detection using subagging ensembles

[1]  Qusay H. Mahmoud,et al.  DReAM: Deep Recursive Attentive Model for Anomaly Detection in Kernel Events , 2019, IEEE Access.

[2]  Konstantina Papagiannaki,et al.  Structural analysis of network traffic flows , 2004, SIGMETRICS '04/Performance '04.

[3]  Kim-Kwang Raymond Choo,et al.  Outlier Dirichlet Mixture Mechanism: Adversarial Statistical Learning for Anomaly Detection in the Fog , 2019, IEEE Transactions on Information Forensics and Security.

[4]  Xin Liu,et al.  Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device , 2019, Ad Hoc Networks.

[5]  Shaoqian Li,et al.  6G Wireless Communications: Vision and Potential Techniques , 2019, IEEE Network.

[6]  Daniel Rosa Canêdo Canêdo,et al.  Intrusion Detection System in Ad Hoc Networks with Artificial Neural Networks and Algorithm K-Means , 2019, IEEE Latin America Transactions.

[7]  Xiangjie Kong,et al.  Spatio-Temporal Network Traffic Estimation and Anomaly Detection Based on Convolutional Neural Network in Vehicular Ad-Hoc Networks , 2018, IEEE Access.

[8]  Biming Tian,et al.  Anomaly detection in wireless sensor networks: A survey , 2011, J. Netw. Comput. Appl..

[9]  K. P. Soman,et al.  Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS) , 2017, Int. J. Inf. Syst. Model. Des..

[10]  S. T. Sarasamma,et al.  Hierarchical Kohonenen net for anomaly detection in network security , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Joel J. P. C. Rodrigues,et al.  Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective , 2019, IEEE Transactions on Multimedia.

[12]  Miguel Nicolau,et al.  Learning Neural Representations for Network Anomaly Detection , 2019, IEEE Transactions on Cybernetics.

[13]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[14]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[15]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[16]  Zhipeng Cai,et al.  CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost , 2012, IEEE Transactions on Parallel and Distributed Systems.

[17]  Wolfgang Kellerer,et al.  Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking , 2016 .

[18]  Rituparna Chaki,et al.  Intrusion Detection in Wireless Ad-Hoc Networks , 2014 .

[19]  Jiankun Hu,et al.  Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[20]  Kun Xie,et al.  A new evolutionary neural networks based on intrusion detection systems using multiverse optimization , 2017, Applied Intelligence.

[21]  Nei Kato,et al.  A Dynamic Anomaly Detection Scheme for AODV-Based Mobile Ad Hoc Networks , 2009, IEEE Transactions on Vehicular Technology.

[22]  Tatsuya Morita,et al.  Traffic Anomaly Detection Based on Robust Principal Component Analysis Using Periodic Traffic Behavior , 2017, IEICE Trans. Commun..

[23]  Joel H. Saltz,et al.  Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce , 2013, Proc. VLDB Endow..

[24]  Beatriz Lorenzo,et al.  Optimal Routing and Traffic Scheduling for Multihop Cellular Networks Using Genetic Algorithm , 2013, IEEE Transactions on Mobile Computing.

[25]  Cheng Yao,et al.  Multi‐scale anomaly detection for high‐speed network traffic , 2015, Trans. Emerg. Telecommun. Technol..

[26]  Wali Khan Mashwani,et al.  A survey on intrusion detection and prevention in wireless ad-hoc networks , 2020, J. Syst. Archit..

[27]  Sébastien Bubeck,et al.  Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..

[28]  Shuokang Huang,et al.  IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks , 2020, Ad Hoc Networks.

[29]  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..

[30]  Chunhui Zhao,et al.  A Spectral–Spatial Method Based on Fractional Fourier Transform and Collaborative Representation for Hyperspectral Anomaly Detection , 2021, IEEE Geoscience and Remote Sensing Letters.

[31]  Wei Chen,et al.  The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.

[32]  Constantinos Marios Angelopoulos,et al.  An architecture for resilient intrusion detection in ad-hoc networks , 2020, J. Inf. Secur. Appl..

[33]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[34]  Song Guo,et al.  Segment-Based Anomaly Detection with Approximated Sample Covariance Matrix in Wireless Sensor Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[35]  Sushil Jajodia,et al.  Detecting VoIP Floods Using the Hellinger Distance , 2008, IEEE Transactions on Parallel and Distributed Systems.

[36]  Jiannong Cao,et al.  Fast Tensor Factorization for Accurate Internet Anomaly Detection , 2017, IEEE/ACM Transactions on Networking.

[37]  Feng Zhao,et al.  An Improved Parallel Network Traffic Anomaly Detection Method Based on Bagging and GRU , 2020, WASA.

[38]  Hongliang Fei,et al.  A Family of Joint Sparse PCA Algorithms for Anomaly Localization in Network Data Streams , 2013, IEEE Transactions on Knowledge and Data Engineering.

[39]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[40]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[41]  Paul Barford,et al.  A signal analysis of network traffic anomalies , 2002, IMW '02.

[42]  P. Bühlmann,et al.  Analyzing Bagging , 2001 .