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Please use this identifier to cite or link to this item: http://hdl.handle.net/1812/391

Title: VISUALIZING NETWORK TRAFFIC AS IMAGES FOR NETWORK ANOMALY DETECTION
Authors: Samabia Tehsin
Dr. Shoab Ahmed Khan
Naveed Sarfraz Khattak
Keywords: Intrusion detection
Network traffic visualization
Denial of service
Probes
Issue Date: 2007
Abstract: This paper presents novel methodology to visualize network traffic. In this paper, method of transforming network packet header data to image is proposed. Methodology to detect anomalies from these images is also projected. This method can be used for real time anomaly detection and intrusion detection. Images can be processed in a number of ways to extract information from it. This formulation enables techniques from image processing to be applied to the analysis of packet header data to reveal interesting properties of traffic. Network anomaly detection systems can also take help from these processes. This method can detect anomalies in an efficient manner and can be used as the basis of number of new anomaly detection methods. Analysis of results of intrusion detection is also presented. This methodology is evaluated using MIT Lincoln Laboratory 1999 DARPA Off-Line Intrusion Detection Evaluation dataset. Our focus here is to develop an innovative technique for network packet header visualization that will highlight the features of the network data most vulnerable to intrusions. Our approach is compared against ALAD and PHAD techniques and results are reported.
Description: Proceeding of the 2nd International Conference on Informatics (Informatics 2007), 27th-28th November 2007, Hilton Petaling Jaya Hotel, Petaling Jaya, Selangor, Malaysia. Page T4-60 - T4-66
URI: http://dspace.fsktm.um.edu.my/handle/1812/391
ISBN: 978-983-43491-1-0
Appears in Collections:Informatics 2007

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