A Machine Learning Approach for Idle State Network Anomaly Detection

Network Anomaly Detection

Two-tier network anomaly detection model: a machine learning approach

Journal of Intelligent Information Systems ◽

10.1007/s10844-015-0388-x ◽

2015 ◽

Vol 48 (1) ◽

pp. 61-74 ◽

Cited By ~ 40

Author(s):

Hamed Haddad Pajouh ◽

GholamHossein Dastghaibyfard ◽

Sattar Hashemi

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Approach ◽

Detection Model ◽

Network Anomaly Detection

A hybrid machine learning approach to network anomaly detection

Information Sciences ◽

10.1016/j.ins.2007.03.025 ◽

2007 ◽

Vol 177 (18) ◽

pp. 3799-3821 ◽

Cited By ~ 178

Author(s):

Taeshik Shon ◽

Jongsub Moon

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Approach ◽

Hybrid Machine ◽

Network Anomaly Detection

Machine Learning Approach for IP-Flow Record Anomaly Detection

NETWORKING 2011 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-20757-0_3 ◽

2011 ◽

pp. 28-39 ◽

Cited By ~ 24

Author(s):

Cynthia Wagner ◽

Jérôme François ◽

Radu State ◽

Thomas Engel

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Approach ◽

2020 5th International Conference on Communication and Electronics Systems (ICCES) ◽

Anomaly detection in Network Traffic Using Unsupervised Machine learning Approach

10.1109/icces48766.2020.9137987 ◽

2020 ◽

Author(s):

Aditya Vikram ◽

Mohana

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Network Traffic ◽

Learning Approach ◽

Unsupervised Machine Learning ◽

Anomaly detection in panoramic dental x-rays using a hybrid Deep Learning and Machine Learning approach

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293765 ◽

2020 ◽

Author(s):

Dhruv Verma ◽

Sunaina Puri ◽

Srikanth Prabhu ◽

Komal Smriti

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Learning Approach ◽

X Rays ◽

A Machine Learning Approach for Background Radiation Modeling and Anomaly Detection in Radiation Time Series pertained to Nuclear Security

10.13182/t123-33516 ◽

2020 ◽

Author(s):

A. Heifetz ◽

M. Alamaniotis

Keyword(s):

Machine Learning ◽

Time Series ◽

Anomaly Detection ◽

Background Radiation ◽

Learning Approach ◽

Nuclear Security ◽

Radiation Modeling ◽

Radiation Time

2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06) ◽

Telemetry-mining: A Machine Learning Approach to Anomaly Detection and Fault Diagnosis for Space Systems

10.1109/smc-it.2006.79 ◽

2006 ◽

Cited By ~ 11

Author(s):

Yoshinobu Kawahara Takehisa Yairi

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Anomaly Detection ◽

Learning Approach ◽

Space Systems ◽

Performance of Interval-Based Features in Anomaly Detection by Using Machine Learning Approach

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2014.v4.427 ◽

2014 ◽

Vol 4 (3) ◽

pp. 292-299 ◽

Cited By ~ 1

Author(s):

Kriangkrai Limthong

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Approach ◽

Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18 ◽

Online anomaly detection for drinking water quality using a multi-objective machine learning approach

10.1145/3205651.3208202 ◽

2018 ◽

Cited By ~ 2

Author(s):

Victor Henrique Alves Ribeiro ◽

Gilberto Reynoso-Meza

Keyword(s):

Machine Learning ◽

Water Quality ◽

Drinking Water ◽

Anomaly Detection ◽

Drinking Water Quality ◽

Learning Approach ◽

Multi Objective ◽

Online Anomaly Detection

2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) ◽

A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system

10.1109/cybersecpods.2018.8560683 ◽

2018 ◽

Cited By ~ 5

Author(s):

Andres Robles-Durazno ◽

Naghmeh Moradpoor ◽

James McWhinnie ◽

Gordon Russell

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Water Supply ◽

Supply System ◽

Water Supply System ◽

Learning Approach ◽

Clean Water ◽

Energy Monitoring ◽