Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

Change-point detection is the problem of finding abrupt changes in time-series. However, the meaningful changes are usually difficult to identify from the original massive traffics, due to high dimension and strong periodicity. In this paper, we propose a novel change-point detection approach, which simultaneously detects change points from all dimensions of the traffics with three steps. We first reduce the dimensions by the classical Principal Component Analysis (PCA), then we apply an extended time-series segmentation method to detect the nontrivial change times, finally we identify the responsible applications for the changes by F-test. We demonstrate through experiments on datasets collected from four distributed systems with 44 applications that the proposed approach can effectively detect the nontrivial change points from the multivariate and periodical traffics. Our approach is more appropriate for mining the nontrivial changes in traffic data comparing with other clustering methods, such as center-based Kmeans and density-based DBSCAN.

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CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500263 ◽

2021 ◽

Vol 30 (05) ◽

pp. 2150026

Author(s):

Haizhou Du ◽

Ziyi Duan ◽

Yang Zheng

Keyword(s):

Time Series ◽

Change Point ◽

Early Stage ◽

Change Point Detection ◽

Change Points ◽

Series Data ◽

Attention Networks ◽

Multi Stage ◽

Detection Approach ◽

Point Detection

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

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A Change-Point Detection Approach to Power Quality Monitoring in Smart Grids

2010 IEEE International Conference on Communications Workshops ◽

10.1109/iccw.2010.5503913 ◽

2010 ◽

Cited By ~ 6

Author(s):

Xingze He ◽

Man-On Pun ◽

C.-C. Jay Kuo ◽

Ye Zhao

Keyword(s):

Power Quality ◽

Change Point ◽

Smart Grids ◽

Change Point Detection ◽

Quality Monitoring ◽

Power Quality Monitoring ◽

Detection Approach ◽

Point Detection

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Slow flooding attack detection in cloud using change point detection approach

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.30.13459 ◽

2018 ◽

Vol 7 (2.30) ◽

pp. 33

Author(s):

Dr Baldev Singh ◽

Dr S.N. Panda ◽

Dr Gurpinder Singh Samra

Keyword(s):

Change Point ◽

Service Providers ◽

Cloud Service ◽

Attack Detection ◽

Change Point Detection ◽

Flooding Attack ◽

Flooding Attacks ◽

Detection Approach ◽

Cloud Service Providers ◽

Point Detection

Cloud computing is one of the high-demand services and prone to numerous types of attacks due to its Internet based backbone. Flooding based attack is one such type of attack over the cloud that exhausts the numerous resources and services of an individual or an enterprise by way of sending useless huge traffic. The nature of this traffic may be of slow or fast type. Flooding attacks are caused by way of sending massive volume of packets of TCP, UDP, ICMP traffic and HTTP Posts. The legitimate volume of traffic is suppressed and lost in traffic flooding traffics. Early detection of such attacks helps in minimization of the unauthorized utilization of resources on the target machine. Various inbuilt load balancing and scalability options to absorb flooding attacks are in use by cloud service providers up to ample extent still to maintain QoS at the same time by cloud service providers is a challenge. In this proposed technique. Change Point detection approach is proposed here to detect flooding DDOS attacks in cloud which are based on the continuous variant pattern of voluminous (flooding) traffic and is calculated by using various traffic data based metrics that are primary and computed in nature. Golden ration is used to compute the threshold and this threshold is further used along with the computed metric values of normal and malicious traffic for flooding attack detection. Traffic of websites is observed by using remote java script.

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