Results 271 to 280 of about 4,030,812 (306)

Change-Point Detection in Dynamic Networks with Missing Links

Operational Research, 2021
Unveiling Hidden Shifts: Detecting Change Points in Dynamic Networks with Missing Links Dynamic networks are ubiquitous in a world increasingly driven by interconnected systems from social media platforms to financial transactions, transportation systems,
F. Enikeeva, O. Klopp
semanticscholar   +1 more source

Online Network DoS/DDoS Detection: Sampling, Change Point Detection, and Machine Learning Methods

IEEE Communications Surveys and Tutorials
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to pose significant threats to networked systems, causing disruptions that can lead to substantial financial losses.
Evans Owusu, Ieee Senthil Mohamed Rahouti Member, Ieee Kumar Jagatheesaperumal Member, Ieee Kaiqi Xiong Senior Member, Member Ieee Yufeng Xin, Lu Lu, Ieee D. Frank Hsu Life Senior Member, M. Rahouti, S. Kumar   +8 more
semanticscholar   +1 more source

A Survey of Change Point Detection in Dynamic Graphs

IEEE Transactions on Knowledge and Data Engineering
Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis.
Yuxuan Zhou, Shang Gao, Dandan Guo, Xiaohui Wei, J. Rokne, Hui Wang   +5 more
semanticscholar   +1 more source

Greedy Kernel Change-Point Detection

IEEE Transactions on Signal Processing, 2019
We consider the problem of detecting abrupt changes in the underlying stochastic structure of multivariate signals. A novel non-parametric and model-free off-line change-point detection method based on a kernel mapping is presented. This approach is sequential and alternates between two steps: a greedy detection to estimate a new breakpoint and a ...
Charles Truong, Laurent Oudre, Nicolas Vayatis   +2 more
openaire   +1 more source

VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks

IEEE Transactions on Information Forensics and Security
Change point detection in dynamic networks aims to detect the points of sudden change or abnormal events within the network. It has garnered substantial interest from researchers due to its potential to enhance the stability and reliability of real-world
Xinxun Zhang, Pengfei Jiao, Mengzhou Gao, Tianpeng Li, Yiming Wu, Huaming Wu, Zhidong Zhao   +6 more
semanticscholar   +1 more source

Change-Point Detection in Angular Data

Annals of the Institute of Statistical Mathematics, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grabovsky, Irina, Horváth, Lajos
openaire   +2 more sources

Hubness Change Point Detection

Proceedings of the AAAI Conference on Artificial Intelligence
This study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search.
Ikumi Suzuki, Kazuo Hara, Eiji Murakami
openaire   +1 more source

Detecting points of change in time series

Computers & Operations Research, 1989
Abstract A performance comparison study of six time-series change detection procedures via forecast-monitoring simulation is presented. Four of the procedures are due to Brown [1], Page [2], Box and Tiao [3] and Gardner [4]. The other two sequential detection schemes are developed in this paper; the first is based on Bagshaw and Johnson [5], while ...
Tep Sastri, Benito Flores, Juan Valdés
openaire   +1 more source

Change‐point detection in panel data

Journal of Time Series Analysis, 2012
We consider N panels and each panel is based on T observations. We are interested to test if the means of the panels remain the same during the observation period against the alternative that the means change at an unknown time. We provide tests which are derived from a likelihood argument and they are based on the adaptation of the CUSUM method to ...
Horváth, Lajos, Hušková, Marie
openaire   +2 more sources

Dynamic Fault Prediction of Power Transformers Based on Lasso Regression and Change Point Detection by Dissolved Gas Analysis

IEEE transactions on dielectrics and electrical insulation, 2020
Practical features of dissolved gases analysis (DGA) are selected and proposed from 62 key gases combinations through maximal information coefficient (MIC) to minimize the influences of random errors and relative percentages variation for field ...
Jun Jiang, Ruyi Chen, Chaohai Zhang, Min Chen, Xiaohan Li, G. Ma   +5 more
semanticscholar   +1 more source