Statistical Techniques for Detecting Traffic Anomalies Through Packet Header Data Project Abstract

The main aim of this project is to design a technique for the anomaly of the traffic detection that is based on the analyzing correlation of the IP destination in the outgoing of traffic at egress router. This data on address correlation are transformed with the use of discrete wavelet for providing effective detection of anomalies with the analysis of the statistics.

The motivation for this project arise from a requirement and it aimed to reduce the chances of intruders from attacking the campus machines. Using this system the campus may prevent hijacking of the system and also the liability for such attacks reduced. Outgoing traffic of header data packet is studied and it includes destination address, number of flows, port numbers etc. it is used in order to find out the anomalies origination at the campus edge of the campus.

Detecting attackers and anomalies that allow close to the main source to put an end to the potential damage that is close to the attacking machines. The technique of traffic monitoring that is close to the source enable quicker identification of the operator. It also allows administrators control domain’s records. By the process of early detection attack propagation speed can be reduced to a large extent.

Our approach monitors the traffic on the network at periodical interval and also analyze it to find if any kind of abnormalities persist in the traffic. Correlating and observing the traffic with its previous conditions it is possible to find out whether the present traffic is behaving in the same manner or not. due to the flash crowds, infrastructure problem and changing of accessing patterns and DoS attacks the network traffic can appear a bit different. The network usage can increased and various abnormalities can even pop up in case of an attack of the bandwidth.

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