As part of a broader organisational restructure, data networking research at Swinburne University of Technology has moved from the Centre for Advanced Internet Architecture (CAIA) to the Internet For Things (I4T) Research Lab.

Although CAIA no longer exists, this website reflects CAIA's activities and outputs between March 2002 and February 2017, and is being maintained as a service to the broader data networking research community.

Winter 2013 Internships at CAIA
Monday June 24th 2013  --  Friday August 2nd 2013

CAIA is offering four internships over the winter 2013 for students who are currently 2 (or more) years through a Telecommunications Engineering, Electronic Engineering or Computer Engineering bachelors degree at an Australian university (or an approved double-degree containing one of those degree programs). The ideal candidate will have a strong interest in IP data networking and be curious about research in Internet protocol design, development and evaluation.

The internship will last for 6 weeks, from Monday June 24th 2013  to Friday August 2nd 2013. Applicants are expected to be available for the entire 6 week period.

Potential applicants should make initial contact with CAIA by midday, Monday May 13th,  2013.

Successful applicants will work under the supervision of the academic staff member associated with the listed projects.


Successful applicants will be assigned to one of the projects listed below based on our evaluation of each applicant's skillset and academic record. Prospective interns should indicate in their application letter their preference for these projects from highest to lowest.


Internships are an opportunity to explore life in a research centre, collaborate with experienced academic staff and do a research project outside the boundaries of a regular undergraduate curriculum.  You will begin to appreciate the combination of discipline and imagination that drives modern data networking research.


A tax-exempt stipend of $475/week will be paid to each intern under this scheme.

This internship is limited to students who meet the following criteria:

The first application round is in four parts. (Yes, this is a modest screening process.)

Note: Applications MUST be in pdf (with .pdf extension) or ASCII text (with .txt extension). Applications submitted in MS Word, OpenOffice, or any other format (including snail mail) will simply be ignored.

Final selection of applicants:

We may choose to use follow-up phone or in-person interviews to make our final selection. More details will be available after May 21st 2013. Where an objective tie-break is required, we will chose the person whose SSH key arrived first (in step 2 above).

Available Projects:

The following projects are available with the nominated academic staff members.

Project 1: Exploring high speed TCP congestion issues in large data centres

Supervisors: Lawrence Stewart, Professor Grenville Armitage

Description:  We propose to evaluate emerging reactive (e.g. faster control loops) and proactive (e.g. burst de-correlation) techniques for managing "incast" TCP congestion events. Transmission Control Protocol (TCP) carries the bulk of all traffic across most common types of data networks, including those in today's high-speed, low-latency datacenters. A key problem faced by commodity datacenters is incast congestion. Client queries are distributed by front-end nodes over TCP connections to multiple back-end servers, and the replies coming back tend to be highly correlated in time. This correlated reply traffic causes microsecond-timescale congestion ("microburst" congestion) in the multi-gigabit Ethernet switch buffers along the return path. TCP reacts far too slowly to packet losses in the context of high-speed and low RTT networks. Consequently, any packet loss caused by incast (microburst) congestion will stall the higher layers' data gathering pipeline (potentially for many tens  of milliseconds) and unnecessarily increase client response times as a result. 

Required skills: Understand how to capture and read IP packet traces with either Ethereal or tcpdump, be capable of installing FreeBSD or Linux onto a regular PC, have a basic understanding of IP networking principles (IPv4 addressing, port numbers, the basic differences between TCP and UDP), understand that 'dir' is not a unix command. Rudimentary coding skills in C, C++ or Java would be beneficial.

Project 2: Low cost home network monitoring using "3D virtual environments"

Supervisor: Professor Grenville Armitage

Description: We propose to explore the practicality of leveraging both 3D online game technology and HTML5/WebGL/WebSockets to provide a more intuitive, qualitative `view' into the state of home networks. Our goal is for end-users to easily observe and adjust the dynamic state of their home network using common laptops, tablets or similar hand held computing devices (such as smartphones). The project will target sub-$100 consumer gateway boxes running an embedded OS (such as OpenWRT/Linux or FreeBSD), and explore the creation of 3D imagery that represents network state in a meaningful manner. 

Required skills: Understand how to capture and read IP packet traces with either Ethereal or tcpdump, be capable of installing FreeBSD or Linux onto a regular PC, have a basic understanding of IP networking principles (IPv4 addressing, port numbers, the basic differences between TCP and UDP), understand that 'dir' is not a unix command. Rudimentary coding skills in C, C++ or Java would be beneficial.

Project 3: Characterising the Statistics of Border Gateway Protocol (BGP) Behaviour

Supervisor: Dr Philip Branch
Description: The core routing protocol of the Internet, 
the Border Gateway Protocol (BGP) is remarkably vulnerable to accidental or deliberate misconfiguration. 
BGP is responsible for distributing routing information between autonomous systems (typically service providers). 
However, BGP generally assumes one autonomous system (AS) can trust what neighbouring ASes tell it about how to reach 
the rest of the Internet. Consequently, whether by malice or mistake, it is easy for individual service providers 
to inject erroneous changes having widespread, sometimes globally negative impact on Internet services. 
The aim of this project is to identify statistical markers that are robust early indicators of such anomalous BGP behaviour. 
In particular we are keen to make use of novel techniques of data analysis suitable for non-linear dynamic systems such as Recurrence Analysis. 

Required skills: Basic data analysis skills using Matlab or similar, understanding of IP networking principles, basic understanding of BGP.

Project 4: Improving Network Traffic Classification with Payload-based Features

Supervisor: Dr Sebastian Zander

Fast, responsive and efficient classification of IP traffic is a key requirement for network security monitoring, lawful interception and quality of service management systems. We have developed Machine Learning (ML) techniques for efficient traffic classification based on statistical traffic features, such as packet inter-arrival time or packet length statistics [1]. Using automatically extracted features based on application protocol data (payload) could further improve the classification accuracy. However, the previously proposed approaches are not scalable and impractical for real-world classifiers on low-cost resource-constrained devices, such as home routers.

The aim of this project is to develop and evaluate a simple more scalable yet effective payload-based feature approach based on our existing real-world ML traffic classifier [1], and to analyse and compare the effectiveness of payload-based and statistical features.


[1] DIstributed Firewall and Flow-shaper Using Statistical Evidence (DIFFUSE),

Required skills: Basic programming skills in C/C++, experience in working with and administrating Unix- based operating systems (e.g. Linux or FreeBSD), basic understanding of IP networking principles (e.g. IP addressing, basic differences between IP, UDP and TCP). Experience with ML or traffic classification is a plus.

Last Updated: Wednesday 1-May-2013 13:37:19 AEST | No longer maintained. Pre-2018 was maintained and authorised by Grenville Armitage,