Detection of variable length anomalous subsequences in data streams
by Amany Abou Safia; Zaher Al Aghbari
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 6, No. 3, 2012

Abstract: We consider the problem of anomaly detection in data streams, which is the problem of extracting subsequences that do not match an expected behaviour. The main challenge for detecting anomalous subsequences from data streams in the existing techniques is to determine the lengths of the normal and anomalous subsequences. Therefore, creating a robust model for detecting the anomalous subsequences is of critical importance. In this paper, we propose an incremental algorithm based on the dynamic time warping technique to detect anomalous subsequences in data streams. The proposed algorithm is able to detect anomalous subsequences under relaxed length constrains of the normal and/or the anomalous subsequences. That is the proposed algorithm is able to detect variable length anomalous subsequences from among variable length normal sequences. The proposed robust model can be applied in areas such as system health monitoring, event detection in sensor networks, and detecting eco-system disturbances, etc. The cost of the proposed algorithm is linear with time and memory.

Online publication date: Sat, 16-Aug-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com