Open Journal Systems at the Victoria University of Wellington Library
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Longitudinal Analysis of SSH Honeypot Logs
Get PDFVisualising attacks and attack patterns from Cowrie SSH honeypots can be challenging when working and handling vast amounts of data over a long period of time. Difficulties can arise when handling complex log files from cowrie honeypots in a JSON format. However, it is important to be able to extract meaningful information to identify trends and patterns performed by attackers over a specified period. A command-line tool was developed using a MapReduce programming model to process large amounts of log data efficiently and in an acceptable timeframe. However, the current solution only visualises features extracted over a short timeframe. Gathering and capturing information over an extended timeline can help identify changes in attackers’ behaviour for specific periods, adding additional information to those already accessible aggregated data. The project applied captured data logs from multiple instances of Cowrie honeypots deployed by the cybersecurity team at Victoria University of Wellington (VUW) and used them to integrate a longitudinal analysis to visualise attack and attack patterns over a long period of time
Modelling BGP Updates for Anomaly Detection using Machine Learning
Get PDFThis paper outlines the problem of detecting and visualising Border Gateway Protocol (BGP) update anomalies in real-time. Detecting and visualising these anomalies in real-time matters because it can help prevent impacts such as denial of service, service slowdown, or loss of revenue that results from accidental or malicious BGP updates. This project solves this problem by creating a program that can visualise router topology and detect BGP anomalies in real-time. Access to historical and live BGP traffic was required, and sourced from the University of Oregon Route Views Project, RIS Route Collectors, and RIS Live. The output of this project is a system that can detect and visualise anomalous BGP updates, allowing network teams to minimise the effect of such anomalies
Landscape through creative eyes: Strands spun by Doris Lusk, Charles Brasch, Bill Sutton, Lyell Cresswell and David Griffiths
Get PDFThis article explores previously undocumented connections between a group of New Zealand landscape paintings and the resulting poems, music and performance. This unplanned creative sequence, spanning 1964-2008, is the work of significant national cultural figures: Doris Lusk, Charles Brasch, W.A. Sutton, David Griffiths with The Ogen Trio, and Lyell Cresswell with the New Zealand Symphony Orchestra. Questions arise around the meaning of creative interpretations of nature and landscape, the interface between different formats, and challenges in confirming the identity of artworks. The watercolours (several held in major institutions) and poems are reproduced, together with other supporting images and documentation.
Histories of Hate: The Radical Right in Aotearoa New Zealand
Get PDFThe contemporary far-right is an extraordinarily complex, diverse ideological and political ecosystem: Christian, atheist, and pagan; violent and parliamentary; nationalist and civilisational; fear- and hate-laden and utopian; individualist and communitarian; traditionalist and accelerationalist; statist and libertarian; progressivist and declinist. Histories of Hate, edited by Matthew Cunningham, Marinus La Rooij, and Paul Spoonley demonstrates that such multiplicity is a feature of the history of the far-right in this country
International Codification: Challenges and Perils
Get PDFThis tribute to Professor Emeritus Tony Smith considers the codification of international law, particularly international criminal law, given Tony's long-term commitment to criminal law. It also evaluates the codification of international law in other areas, addressing success and failure
A Former Principal’s View and Experience of the Ministry of Education’s Involvement in Contemporary Education Issues in New Zealand
Get PDFThis commentary provides a former principal’s perspective on contemporary educational issues and the role of the Ministry of Education in addressing these. In the author’s experience, when the Ministry of Education works with principals, positive changes have begun to happen, although often not to the stage of embedding these changes. Problems can emerge when the Ministry of Education makes decisions without involving the sector
Smart Temperature Sensor Hot Water Cylinder Demand Management
Get PDFThe growth of residential electricity consumption in Aotearoa urges for the improvement of existing demand management technologies. Expensive technologies like Ripple Control externally control residential electric hot water cylinders to help manage demand. This paper presents and evaluates an affordable alternative hot water controller by using an energy harvesting Internet of Things temperature control sensor found at the outlet pipe of a hot water cylinder. The control sensor is charged by harvesting the thermal energy from the temperature gradient between the outlet pipe and the ambient air using mounted Thermoelectric Generators, or TEGs. Using Bluetooth Low-Energy the control sensor relays accurate temperature readings of the stored hot water. The control sensor’s power electronic interface for the TEGs is a low power DC-DC boost converter that charges a lithium- ion battery. The controls sensor’s estimated energy consumption was simulated and used for the design and development of the device. The expected energy consumption was later evaluated with the recorded consumption. Thermal data gathered from an electric hot water cylinder and thermal modelling software were used to evaluate the performance of the TEGs to be selected meeting the energy demands of the control sensor. The boost converter design was simulated and developed. The real performance of the boost converter and TEGs were then tested and evaluated with the simulated results. The paper investigates the lifetime, temperature reading accuracy and network performance of the control sensor. A final economic evaluation is also performed on the control sensor. It is found that the controller is technically and economically viable in Aotearoa New Zealand
IoT Workflow Scheduling in Cloud and Edge
Get PDFIoT devices are becoming more and more common every day and play a huge part of our everyday lives and with these devices often having low processing power and availability to resources they also make use of edge and cloud servers. With all this information needed to be processed from different devices, it must be decided on what and when they are processed. From this, the IoT workflow scheduling problem is where we must organize through the workflows that are generated from various IoT devices and decide how they should be executed. To solve this problem this project proposes a Particle Swarm Optimization (PSO) based algorithm that can find a near-optimal solution to IoT workflow scheduling in cloud and edge. This solution is developed to focus on reducing both the energy and the makespan of executing a set of workflows. With this proposed solution we thoroughly evaluate and compare its performance against already existing methods using benchmark workflows showing the algorithm ability to efficiently find a near optimal solution
Genetic Programming Hyper-Heuristic for Emergency Medical Dispatching
Get PDFEmergency Medical Dispatch (EMD) defines the healthcare task concerning the assignment of paramedic resources (equipment and people) to emergencies in the community. Due to the random and unpredictable nature of such tasks, EMD is highly dynamic. Research exists using Machine Learning to automatically learn heuristics for EMD via the Genetic Programming Hyper Heuristic (GPHH) technique. This project seeks to improve upon such existing research by implementing multi-fidelity techniques. In particular, by training GPHH models on city networks (graphs) of varying fidelity. The models produce lower-fidelity graphs by subdividing a source graph into boxes, and quantising locations within each box to a single location. The fidelity of graphs is then dictated by the size of the subdivided boxes. The computation savings afforded by multi-fidelity techniques decrease model training time on graphs generated from real-world ambulance coverage areas, whilst not significantly impacting training or test performance. The multi-fidelity models were evaluated against pre-existing models on two real-world graphs, the Wellington and Christchurch ambulance coverage areas, to investigate the feasibility of applying multi-fidelity techniques to EMD
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