A Novel Fuzzy Anomaly Detection Method Based on Clonal Selection Clustering Algorithm

2006 ◽  
pp. 642-651
Author(s):  
Fenghua Lang ◽  
Jian Li ◽  
Yixian Yang
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Clonal Selection

scholarly journals An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhenlun Yang
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Binary Classification ◽  
Simple Method ◽  
Gait Patterns ◽  
Abnormal Gait ◽  
Gait Features ◽  
Efficient Gait ◽  
Semisupervised Clustering

The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before ahead. Moreover, the proposed method does not need any parameter settings by users and can start producing detection results under the work by only collecting a very small number of gait samples, even though none of those gait samples are abnormal. Therefore, the proposed method can provide fast and simple deployment for various anomaly gait detection application scenarios. The proposed method is composed of two main modules: (1) feature extraction from gait images and (2) anomaly detection via binary classification. In the first module, a new representation of the most frequently involved area of the silhouette gait images called full gait energy image (F-GEI) is proposed. Furthermore, based on the F-GEI, a novel and simple method characterizing individual walking properties is developed to extract gait features from individual subjects. In the second module, based on the very limited prior knowledge on the target dataset, a semisupervised clustering algorithm is proposed to perform the binary classification for detecting the gait anomaly of each subject. The performance of the proposed gait anomaly detection method was evaluated on the human gaits dataset in comparison with three state-of-the-art methods. The experiment results show that the proposed method is an effective and efficient gait anomaly detection method in terms of accuracy, robustness, and computational efficiency.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors

Adaptive anomaly detection method of Web-based attacks

2013 ◽  
Vol 32 (7) ◽  
pp. 2003-2006
Author(s):  
Kai WEN ◽  
Fan GUO ◽  
Min YU
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Web Based

WSAD: An Unsupervised Web Session Anomaly Detection Method

2020 ◽  
Author(s):  
Yizhen Sun ◽  
Yiman Xie ◽  
Weiping Wang ◽  
Shigeng Zhang ◽  
Jun Gao ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Detection Method
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