Network Traffic Deviation Detection Based on Fractal Dimension
|
| Title | Network Traffic Deviation Detection Based on Fractal Dimension |
| Authors | |
| Abstract | In this paper we examine aggregate network traffic for deviation detection. The precise and fast detection of network traffic deviation is crucial to improve the efficient operation of a network. It is often difficult to detect the time when the defects occur in a network. In this article, a new algorithm is bestowed to supervise the aggregate network traffic to fast detect the time deviation transpires in a network. This is performed by supervising the statistical attributes of the time series depicting the network conduct. The procedure examines the network conduct using fractal dimension and discrete stationary wavelet transform. In the suggested procedure, after implementing discrete stationary wavelet transform on the signal depicting the network traffic, the fractal dimension of the disintegrated signal is computed in a sliding window. Then, variations of signal fractal dimension are regarded for deviation detection. Performance of the suggested procedure is compared with that of three other existent procedures using artificial substance signal .The results show superiority of the suggested procedure in terms of preciseness compared to existent procedures. |
| Publisher | University of Zagreb, University Computing Centre - SRCE |
| Date | 2012-06-05 |
| Source | Journal of Computing and Information Technology Vol 20, No 1 (2012) |
| Rights | CIT. Journal of Computing and Information Technology is an open access journal.Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work´s authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal´s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). |