Spatio-Temporal Analysis of Network Traffic Test phase, srs, design phase and source code final deliverable

Spatio-Temporal Analysis of Network Traffic Test phase, srs, design phase and source code final deliverable

Project Domain / Category

Computer Networks

Abstract /

Introduction

The knowledge of network traffic behavior is important for traffic engineering tasks such as link capacity planning, traffic classification, and anomaly detection. Traffic characterization is typically addressed through statistical analysis of individual link(s) and network-wide traffic volume properties such as counts of bytes and packets as well as by analyzing the distributional behavior of particular packet header fields. This project aims to develop a software solution to analyze the network traffic to identify the spatio-temporal features. The network traffic captures can be downloaded from the data source given in the tools section. Any other data source can also be used. Functional requirements:

Students will be required to decode the pcap files and investigate the spatial and temporal features of network traffic and develop an appropriate software solution to analyze the traffic from data sources given in this document.

The software application should be able to:

1. Read the pcap files and extract the header information such as timestamps, source/destination addresses, TCP port, Packet Size, Type of Packet and other relevant information.
2. Store the extracted header information in a persistent database or text file such as csv.
3. Allow the users to split the large size pcap files into smaller size based on file size and time duration.
4. Identify top flows (highest throughput) for user traffic and network control plane traffic based on followings
   1. Source and destination with TCP port
   2. application type
   3. Session length
5. Measure the flow similarity across daily/weekly traffic captures.
6. Approximate data rate probability distribution for distinct flows for daily traffic.
7. Approximate interpacket delay distribution on daily traffic.
8. Produce analysis of at least one-week traffic captures of fifteen minutes from reputable internet traffic sources.

Tools:

Python/Java, C/C++ (other programming languages could also be used.)

And IDE of choice

Data Source: http://mawi.wide.ad.jp/mawi/samplepoint-G/2020/