A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time ...
Anomaly detection, or outlier detection, is important because it provides meaningful, often crucial, actionable information for a wide variety of application domains. Grubbs (1969) proposed one of the first definitions of an outlier in the statistical literature: “An outlying observation, or ‘...
Online Clustering and Detective Cost Based Anomaly Detection Scheme for MANET基于在线聚类和检测成本的移动自组网异常检测 WANG Lei-chun,MA Chuan-xiang,王雷春,马传香 Keywords: Mobile ad hoc networks,Online clustering,Detective cost,Anomaly detection移动自组网,在线聚类,检测成本,异常检测 Full-Text Cite ...
Wang Lifang, Han Xie. Anomaly Intrusion Detection Based on Quotient Space Granularity Clustering. Computer Applications and Software, 2010, 27(5) : 1911-1913 ( in Chinese) (王丽芳,韩燮. 基于商空间粒度聚类的异常入侵检测. 计算 机应用与软件, 2010, 27(5) : 1911-1913)...
Jae-Chul Kim, Do-Hyeun Kim First published:05 March 2025 https://doi.org/10.1049/cit2.70000 Read thefull text PDF Tools Share Abstract In recent years, there has been a concerted effort to improve anomaly detection techniques, particularly in the context of high-dimensional, distributed clinical...
In recent years, there has been a concerted effort to improve anomaly detection techniques, particularly in the context of high-dimensional, distributed clinical data. Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy, personalising treatment plans, ...
With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clustering-based...
Time series data analysis, especially forecasting, classification, imputation, and anomaly detection, has gained a lot of research attention in recent years due to its prevalence and wide application. Compared to classification, clustering is an unsupervised task and thus more applicable for analyzing ...
2.1. Classify according to the Principle of Detection (1) Anomaly detection: anomaly detection first summarizes the characteristics of normal behavior and then judges whether the user behavior is intrusive according to the user’s activities or the use of resources in the system. Anomaly detection...
An anomaly intrusion detection algorithm based on semi-supervised clustering along with k-nearest neighbor was presented. It could solve the problem of the insufficiency of training samples that the intrusion detection algorithms based on supervised learning face. The algorithm exploited minimal labeled ...