NetFlowMatrix: a visual approach for analysing large NetFlow data
by Yingjie Chen; Baijian Yang; Weijie Wang
International Journal of Security and Networks (IJSN), Vol. 12, No. 4, 2017

Abstract: NetFlowMatrix is a visual analytics system design that adopts small multiple charts to help analysts monitor NetFlow data of a computer network. This design provides an overview and drill-down interactions that allow analysts to see and analyse traffic data from a computer network of thousands of computers and millions of flow records. Various network activities generate NetFlow records with different characteristics. We grouped network flow information into a matrix of cells to display aggregate flows based on payload size and flow duration. The aggregate overview method is scalable that allows the design to support much larger computer networks. To visually distinguish extreme low and high quantity of flows, we use colour shades to distinguish different scales of cells. Utilising this innovative overview design, professionals can easily identify patterns and instances, obvious or subtle, from a large number of network flows.

Online publication date: Fri, 24-Nov-2017

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 Security and Networks (IJSN):
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