ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC

AnomayDetect:An online distance-based anomaly detection algorithm

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.07.005
  • Received Date: 25 September 2018
  • Rev Recd Date: 04 December 2018
  • Publish Date: 31 July 2019
  • Anomaly detection is a key challenge in data mining which has a wide range of applications in the field of the Internet, including network security, image recognition and intelligent operation. In particular, intelligent operation has made great progress in recent years. Existing anomaly detection algorithms have many problems, such as low accuracy and inability to update automatically. The problem of anomaly detection in the context of intelligent operation and a practical need for high-accuracy, online and universal anomaly detection algorithms is studied. Based on the existing algorithms, an online distance-based anomaly detection algorithm is identified. Through the experiments on Yahoo Web-scope S5 dataset it is shown that the algorithm can detect anomalies successfully. A comparative study of several anomaly detectors verifies the effectiveness of the proposed algorithm.
    Anomaly detection is a key challenge in data mining which has a wide range of applications in the field of the Internet, including network security, image recognition and intelligent operation. In particular, intelligent operation has made great progress in recent years. Existing anomaly detection algorithms have many problems, such as low accuracy and inability to update automatically. The problem of anomaly detection in the context of intelligent operation and a practical need for high-accuracy, online and universal anomaly detection algorithms is studied. Based on the existing algorithms, an online distance-based anomaly detection algorithm is identified. Through the experiments on Yahoo Web-scope S5 dataset it is shown that the algorithm can detect anomalies successfully. A comparative study of several anomaly detectors verifies the effectiveness of the proposed algorithm.
  • loading
  • 加载中

Catalog

    Article Metrics

    Article views (325) PDF downloads(533)
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return