EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection

Collective Anomaly Detection Techniques for Network Traffic Analysis

Annals of Data Science ◽

10.1007/s40745-018-0149-0 ◽

2018 ◽

Vol 5 (4) ◽

pp. 497-512 ◽

Cited By ~ 10

Author(s):

Mohiuddin Ahmed

Keyword(s):

Anomaly Detection ◽

Network Traffic ◽

Traffic Analysis ◽

Detection Techniques ◽

Network Traffic Analysis ◽

Collective Anomaly

Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks

Future Data and Security Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-319-48057-2_9 ◽

2016 ◽

pp. 141-152 ◽

Cited By ~ 44

Author(s):

Loïc Bontemps ◽

Van Loi Cao ◽

James McDermott ◽

Nhien-An Le-Khac

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Collective Anomaly

Deep learning for collective anomaly detection

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2020.105220 ◽

2020 ◽

Vol 21 (1) ◽

pp. 137

Author(s):

Mohiuddin Ahmed ◽

Al Sakib Khan Pathan

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Collective Anomaly

A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security

IEEE Access ◽

10.1109/access.2019.2909750 ◽

2019 ◽

Vol 7 ◽

pp. 49157-49168 ◽

Cited By ~ 3

Author(s):

Yu Weng ◽

Lei Liu

Keyword(s):

Anomaly Detection ◽

Mobile Service ◽

Detection Approach ◽

Collective Anomaly

Collective Anomaly Detection: Application to Respiratory Artefact Removals

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings - Springer Theses ◽

10.1007/978-3-319-98675-3_5 ◽

2018 ◽

pp. 49-81

Author(s):

Thuy T. Pham

Keyword(s):

Anomaly Detection ◽

Respiratory Artefact ◽

Collective Anomaly

A novel approach to identify regional fault of urban power grid based on collective anomaly detection

International Journal of Modelling Identification and Control ◽

10.1504/ijmic.2020.10038194 ◽

2020 ◽

Vol 36 (1) ◽

pp. 78

Author(s):

Zhongfeng Hu ◽

Minglun Ren ◽

Xiaodi Huang

Keyword(s):

Anomaly Detection ◽

Power Grid ◽

Novel Approach ◽

Collective Anomaly ◽

Grid Based

Collective Anomaly Detection for Multivariate Data using Generative Adversarial Networks

2020 International Conference on Computational Science and Computational Intelligence (CSCI) ◽

10.1109/csci51800.2020.00106 ◽

2020 ◽

Author(s):

Chihiro Maru ◽

Ichiro Kobayashi

Keyword(s):

Anomaly Detection ◽

Multivariate Data ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Collective Anomaly

Multi-task Multi-modal Models for Collective Anomaly Detection

2017 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm.2017.27 ◽

2017 ◽

Cited By ~ 2

Author(s):

Tsuyoshi Ide ◽

Dzung T. Phan ◽

Jayant Kalagnanam

Keyword(s):

Anomaly Detection ◽

Collective Anomaly

Random Partitioning Forest for Point-Wise and Collective Anomaly Detection—Application to Network Intrusion Detection

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2021.3050605 ◽

2021 ◽

Vol 16 ◽

pp. 2157-2172

Author(s):

Pierre-Francois Marteau

Keyword(s):

Intrusion Detection ◽

Anomaly Detection ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Collective Anomaly

A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

E3S Web of Conferences ◽

10.1051/e3sconf/202125201052 ◽

2021 ◽

Vol 252 ◽

pp. 01052

Author(s):

Zhongfeng Hu ◽

Xiaodi Huang

Keyword(s):

Fault Diagnosis ◽

Anomaly Detection ◽

Data Distribution ◽

Likelihood Estimate ◽

Iteration Algorithm ◽

Multivariate Gaussian Distribution ◽

Test Dataset ◽

Sample Data ◽

Using Data ◽

Collective Anomaly

Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.