Parallelizing Incremental Grid Density Clustering Algorithm for Real-time Network Anomaly Detection

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2019.01.03 ◽

2019 ◽

Vol 10 (1) ◽

pp. 71-76

Author(s):

Jagatheesan Kunasaikaran ◽

Roslan Ismail ◽

Abdul Rahim Ahmad

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Clustering Algorithm ◽

Density Clustering ◽

Network Anomaly Detection

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Multi-density Clustering Algorithm for Anomaly Detection Using KDD’99 Dataset

Advances in Computing and Communications - Communications in Computer and Information Science ◽

10.1007/978-3-642-22709-7_60 ◽

2011 ◽

pp. 619-630

Author(s):

Santosh Kumar ◽

Sumit Kumar ◽

Sukumar Nandi

Keyword(s):

Anomaly Detection ◽

Clustering Algorithm ◽

Density Clustering

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Visualize Network Anomaly Detection by Using K-Means Clustering Algorithm

International journal of Computer Networks & Communications ◽

10.5121/ijcnc.2013.5514 ◽

2013 ◽

Vol 5 (5) ◽

pp. 195-208 ◽

Author(s):

A. M. Riad ◽

Ibrahim Elhenawy ◽

Ahmed Hassan ◽

Nancy Awadallah

Keyword(s):

Anomaly Detection ◽

Clustering Algorithm ◽

Network Anomaly Detection

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Catch It If You Can: Real-Time Network Anomaly Detection with Low False Alarm Rates

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) ◽

10.1109/icmla.2017.00-36 ◽

2017 ◽

Author(s):

Georgios Kathareios ◽

Andreea Anghel ◽

Akos Mate ◽

Rolf Clauberg ◽

Mitch Gusat

Keyword(s):

Anomaly Detection ◽

False Alarm ◽

Real Time ◽

False Alarm Rates ◽

Network Anomaly Detection

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Real-time network anomaly detection architecture based on frequent pattern mining technique

2013 International Conference on Research and Innovation in Information Systems (ICRIIS) ◽

10.1109/icriis.2013.6716742 ◽

2013 ◽

Author(s):

Aiman Moyaid Said ◽

Dhanapal Durai Dominic ◽

Ibrahima Faye

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Mining Technique ◽

Network Anomaly Detection

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Real-time network anomaly detection system using machine learning

2015 11th International Conference on the Design of Reliable Communication Networks (DRCN) ◽

10.1109/drcn.2015.7149025 ◽

2015 ◽

Author(s):

Shuai Zhao ◽

Mayanka Chandrashekar ◽

Yugyung Lee ◽

Deep Medhi

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Real Time ◽

Detection System ◽

Network Anomaly Detection ◽

Anomaly Detection System

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Artificial immune K ‐means grid‐density clustering algorithm for real‐time monitoring and analysis of urban traffic

Electronics Letters ◽

10.1049/el.2013.2514 ◽

2013 ◽

Vol 49 (20) ◽

pp. 1272-1273 ◽

Author(s):

Chuan Ming Chen ◽

Dechang Pi ◽

Zhuoran Fang

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Urban Traffic ◽

Real Time Monitoring ◽

Artificial Immune ◽

Density Clustering

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A Grid-Based Clustering Algorithm for Network Anomaly Detection

The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007) ◽

10.1109/isdpe.2007.110 ◽

2007 ◽

Author(s):

Xiaotao Wei ◽

Houkuan Huang ◽

Shengfeng Tian

Keyword(s):

Anomaly Detection ◽

Clustering Algorithm ◽

Network Anomaly Detection ◽

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Resilient real-time network anomaly detection using novel non-parametric statistical tests

Computers & Security ◽

10.1016/j.cose.2020.102146 ◽

2021 ◽

Vol 102 ◽

pp. 102146

Author(s):

Chad A. Bollmann ◽

Murali Tummala ◽

John C. McEachen

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Statistical Tests ◽

Network Anomaly Detection ◽

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Real-Time Dynamic Network Anomaly Detection

IEEE Intelligent Systems ◽

10.1109/mis.2018.022441346 ◽

2018 ◽

Vol 33 (2) ◽

pp. 5-18 ◽

Author(s):

Jordan Noble ◽

Niall Adams

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Dynamic Network ◽

Time Dynamic ◽

Network Anomaly Detection

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Unsupervised Network Anomaly Detection in Real-Time on Big Data

Communications in Computer and Information Science - New Trends in Databases and Information Systems ◽

10.1007/978-3-319-23201-0_22 ◽

2015 ◽

pp. 197-206 ◽

Author(s):

Juliette Dromard ◽

Gilles Roudière ◽

Philippe Owezarski

Keyword(s):

Big Data ◽

Anomaly Detection ◽

Real Time ◽

Network Anomaly Detection

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