Nearest cluster-based intrusion detection through convolutional neural networks

[1]  Dong Jin,et al.  A Comparative Study of Off-Line Deep Learning Based Network Intrusion Detection , 2018, 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN).

[2]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[3]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  David D. Cox,et al.  Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.

[5]  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).

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Xu Chen,et al.  Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data , 2019, IEEE Access.

[8]  Nour Moustafa,et al.  Identification of malicious activities in industrial internet of things based on deep learning models , 2018, J. Inf. Secur. Appl..

[9]  Djamal Zeghlache,et al.  A Cascade-structured Meta-Specialists Approach for Neural Network-based Intrusion Detection , 2019, 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[10]  Lei Shi,et al.  MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks , 2019, ICANN.

[11]  Victor C. M. Leung,et al.  Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System Over Big Data , 2018, IEEE Access.

[12]  K. V. N. Sunitha,et al.  Effective discriminant function for intrusion detection using SVM , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[13]  Ahmed Elsherif,et al.  Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm , 2018 .

[14]  Yixian Yang,et al.  Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks , 2019, Applied Sciences.

[15]  Xue Wang,et al.  Comparison deep learning method to traditional methods using for network intrusion detection , 2016, 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN).

[16]  Ethem Alpaydin,et al.  Unsupervised feature extraction with autoencoder trees , 2017, Neurocomputing.

[17]  K. P. Soman,et al.  Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.

[18]  Tao Feng,et al.  Statistics-Enhanced Direct Batch Growth Self-Organizing Mapping for Efficient DoS Attack Detection , 2019, IEEE Access.

[19]  Kai Huang,et al.  Intrusion Detection Using Convolutional Neural Networks for Representation Learning , 2017, ICONIP.

[20]  Yu-Lin He,et al.  Fuzziness based semi-supervised learning approach for intrusion detection system , 2017, Inf. Sci..

[21]  Chuan Sheng Foo,et al.  Adversarially Learned Anomaly Detection , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[22]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[23]  Cheng-Chew Lim,et al.  Using Convolutional Neural Networks for Classifying Malicious Network Traffic , 2019, Deep Learning Applications for Cyber Security.

[24]  Jinoh Kim,et al.  An Encoding Technique for CNN-based Network Anomaly Detection , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[25]  Muhammad Munwar Iqbal,et al.  Enhanced Network Anomaly Detection Based on Deep Neural Networks , 2018, IEEE Access.

[26]  Michael P. Cohen,et al.  Stratified Sampling , 2022, The SAGE Encyclopedia of Research Design.

[27]  Yunbin He.  Identification and Processing of Network Abnormal Events Based on Network Intrusion Detection Algorithm , 2019, Int. J. Netw. Secur..

[28]  Abdallah Moubayed,et al.  Clustering Enabled Classification using Ensemble Feature Selection for Intrusion Detection , 2019, 2019 International Conference on Computing, Networking and Communications (ICNC).

[29]  Pasquale Malacaria,et al.  Malware Detection Using 1-Dimensional Convolutional Neural Networks , 2019, 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[30]  Yuancheng Li,et al.  A Hybrid Malicious Code Detection Method based on Deep Learning , 2015 .

[31]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[33]  Adel Binbusayyis,et al.  Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach , 2019, IEEE Access.

[34]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[35]  Jaime Lloret,et al.  Shallow neural network with kernel approximation for prediction problems in highly demanding data networks , 2019, Expert Syst. Appl..

[36]  Corrado Loglisci,et al.  Exploiting the Auto-Encoder Residual Error for Intrusion Detection , 2019, 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[37]  Tayeb Kenaza,et al.  An efficient hybrid SVDD/clustering approach for anomaly-based intrusion detection , 2018, SAC.

[38]  Donato Malerba,et al.  Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification , 2020, Complex Pattern Mining.

[39]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[40]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[41]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[42]  Daniel S. Berman,et al.  A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..

[43]  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).

[44]  André C. Drummond,et al.  Adaptive anomaly‐based intrusion detection system using genetic algorithm and profiling , 2018, Secur. Priv..

[45]  Karel Macek,et al.  Pareto Principle in Datamining: an Above-Average Fencing Algorithm , 2008 .

[46]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[47]  Abderrahim Benslimane,et al.  Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN , 2018, 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM).

[48]  Jinoh Kim,et al.  An Empirical Study on Network Anomaly Detection Using Convolutional Neural Networks , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[49]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[50]  Kaushik Roy,et al.  LSTM for Anomaly-Based Network Intrusion Detection , 2018, 2018 28th International Telecommunication Networks and Applications Conference (ITNAC).

[51]  Jacek Rumiński,et al.  A survey of neural networks usage for intrusion detection systems , 2020, Journal of Ambient Intelligence and Humanized Computing.

[52]  Julio Ortega Lopera,et al.  PCA filtering and probabilistic SOM for network intrusion detection , 2015, Neurocomputing.

[53]  Jian Sun,et al.  Convolutional neural networks at constrained time cost , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).