An augmented K-means clustering approach for the detection of distributed denial-of-service attacks

[1]  Wesam Bhaya,et al.  DDoS attack detection approach using an efficient cluster analysis in large data scale , 2017, 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT).

[2]  Stephen Becker,et al.  Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means , 2015, IEEE Transactions on Information Theory.

[3]  Nhien-An Le-Khac,et al.  DDoSNet: A Deep-Learning Model for Detecting Network Attacks , 2020, 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[4]  Ali A. Ghorbani,et al.  Towards a Reliable Intrusion Detection Benchmark Dataset , 2017 .

[5]  Divakar Singh,et al.  Performance Evaluation of K-Means and Heirarichal Clustering in Terms of Accuracy and Running Time , 2012 .

[6]  Manish Kumar,et al.  A review of detection approaches for distributed denial of service attacks , 2017 .

[7]  Jeffrey L. Gunter,et al.  Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks , 2018, SASHIMI@MICCAI.

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

[9]  Soodeh Hosseini,et al.  The hybrid technique for DDoS detection with supervised learning algorithms , 2019, Comput. Networks.

[10]  Guodong Han,et al.  Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System , 2018, IEEE Access.

[11]  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.

[12]  Yang Xiao,et al.  A survey of distributed denial-of-service attack, prevention, and mitigation techniques , 2017, Int. J. Distributed Sens. Networks.

[13]  Ali Dehghantanha,et al.  Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing , 2016, EURASIP Journal on Wireless Communications and Networking.

[14]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Gu Yonghao,et al.  Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm , 2019 .

[16]  A. Arfeen,et al.  A generalized machine learning‐based model for the detection of DDoS attacks , 2020, Int. J. Netw. Manag..

[17]  Yu Lasheng,et al.  Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection , 2018, IEEE Access.

[18]  Chien-Hsing Chou,et al.  Short Papers , 2001 .

[19]  Ali A. Ghorbani,et al.  Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy , 2019, 2019 International Carnahan Conference on Security Technology (ICCST).

[20]  Mugdha Jain,et al.  Adapting k-means for Clustering in Big Data , 2014 .

[21]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[22]  Gui-Bin Bian,et al.  Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications , 2018, IEEE Access.

[23]  Marius Leordeanu,et al.  Unsupervised Learning Towards the Future , 2020 .

[24]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[25]  Ali Selamat,et al.  Adaptive feature selection for denial of services (DoS) attack , 2017, 2017 IEEE Conference on Application, Information and Network Security (AINS).

[26]  Satyajit Yadav,et al.  Detection of Application Layer DDoS attack by feature learning using Stacked AutoEncoder , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[27]  Arwa Alrawais,et al.  FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks , 2020, IEEE Internet of Things Journal.

[28]  Jugal Kalita,et al.  Active learning to detect DDoS attack using ranked features , 2019, Comput. Commun..

[29]  Yuancheng Li,et al.  Learning Multilevel Auto-Encoders for DDoS Attack Detection in Smart Grid Network , 2019, IEEE Access.