Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal
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 ‘...
Wu Ningning,Zhang Jing.Factor-analysis based anomaly detection and clustering.Decision Sup-port Systems. 2006Wu Ningning,Zhang Jing. Factor-analysis based anomaly detection and clustering[J]. Decision Sup- port Systems. 2006,42(1):375～389....
This paper presents an anomaly detection approach based on clustering and classification for intrusion detection (ID). We use connections obtained from raw packet data of the audit trail as basic elements, then map the network connection records into 8 f
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...
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 ...
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, ...
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)...
We show by experiments that the proposed method outperforms competing subspace outlier detection approaches on real world data sets.Similar content being viewed by others Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation Article Open access 27 November...