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Shadow IT discovery and attack surface detection for small businesses.

In larger enterprises, it is not uncommon to see the concurrent implementation of CMDB (Configuration Management DB), CSPM (Cloud Security Posture Management), and ASM (Attack surface management). This can be …

In larger enterprises, it is not uncommon to see the concurrent implementation of CMDB (Configuration Management DB), CSPM (Cloud Security Posture Management), and ASM (Attack surface management). This can be a costly and time consuming capabilities that most small-medium enterprises (SME) that are limited in resources (financially, access to expertise, time limitation) would not be able to develop/procured. For SME that are not fully leveraging cloud as their compute layer, will have on-premises infrastructure that are not asset managed, not hardened, and lack vulnerability detection. Although there are multiple open-source tools that can be used, most SME owners/operators do not have the expertise to "establish them". The idea is to create an SaaS orchestration platform that will take in telemetry from open source tools, enrich with open threat intel, and provide the SME owners/operators with insights to their environment and remote security operations personnel to access in a secure manner. The platform will before automated, scheduled or ad-hoc scanning of the environment to detect shadow IT and exposure in their environment. As an advisory firm, this will enable our team to manage multiple client using a common interface.

Client: Joshua Qwek

Location: Perth

Multiple Teams

IP: An agreement to joint exploitation of any IP that is created

AI Unit Rule Builder

The aim of this project is to develop an AI-powered tool that helps users construct unit rules (prerequisites, corequisites, and incompatibilities) through an interactive, step-by-step process. The tool will use …

The aim of this project is to develop an AI-powered tool that helps users construct unit rules (prerequisites, corequisites, and incompatibilities) through an interactive, step-by-step process. The tool will use AI to generate a series of conversational, dynamic questions that adjust based on the user’s previous answers. Based on these inputs, the tool will then generate the appropriate format for unit rules. The output will be available in both a human-readable format and a structured format, such as a database structure. The suggestions can be powered by either AI or algorithmic approaches. Furthermore, the tool will include an upload feature to allow users to import existing unit rules and the ability to use AI to recreate and modify those rules. Project Tasks Project requirements gathering and initial designing, defining the AI model and technologies to be used and defining the scope of input data (list of units and existing unit rules and courses) AI Model Training & Integration UI/UX Design: Design a user-friendly interface where user can interact with the tool and answer dynamic questions. Create an intuitive interface for uploading unit rules. Design a modification interface to let the user adjust and update the generated rule. Development of other functionalities Develop AI based, Dynamic Question Flow AI/Algorithmic Suggestions for Unit Rules: develop algorithms to suggest valid unit rule structures based on user inputs. Upload functionality Output generation

Client: Feba Chacko

Location: You can also contact [email protected] or [email protected]

IP: Creative Commons (open source)

Automated HPTLC Database Screening for Compound Identification

The UWA Honey Research lab has established a comprehensive High Performance Thin Layer Chromatography (HPTLC) derived database on a large number of phenolic standards. The captured information (in Excel) includes …

The UWA Honey Research lab has established a comprehensive High Performance Thin Layer Chromatography (HPTLC) derived database on a large number of phenolic standards. The captured information (in Excel) includes Rf values for two different mobile phases, RGB values at 254 and 366 nm, RGB values at 366 nm and white light after derivatisation with two different reagents as well as the respective UV- and fluorescence spectra's maxima and minima. This information can be used to screen constituents in honey extracts for potential matches with the database following a validated protocol, thus aiding in chemical compound identification. Potential hits are confirmed using spectral overlays. The establishment, validation and application of the database is described in the following paper: https://www.mdpi.com/1420-3049/27/19/6651 Given the wealth of data used in this approach confirmed matches are more reliable than those obtained with other analytical methods (e.g. HPLC-DAD). However, manual screening of sample data against the database is time consuming and would therefore benefit from an automated approach. Ideally, we envisage an automated system where key parameters of a compound of interest are inputted to automatically generate potential matches from the database including spectral overlays. Such an automated approach would greatly facilitate the identification of honey constituents and therefore benefit authentication and quality control efforts of the honeybee industry. By extension, the approach could also be adapted for other natural product classes, thus opening up new avenues for compound identification.

Client: Connie Locher

Location: Department of Pharmacy, School of Health and Clinical Sciences, Curnow Building, UWA

IP: Creative Commons (open source)

Cyber Secure by Design

The Uni IT Cyber Team offer Cyber Advisory and Architecture services to a number of different stakeholders to ensure that the solutions they are procuring or building are secure by …

The Uni IT Cyber Team offer Cyber Advisory and Architecture services to a number of different stakeholders to ensure that the solutions they are procuring or building are secure by design. These advisory services are underpinned by a UWA Cyber framework which includes a cyber controls catalogue, standards, guidelines and toolkit of key controls that are applied to solution based on their assessed criticality. A large portion of this service is manual, from engagement to assessment and also reporting of key metrics. There is an opportunity and desire for us to refine and optimise parts of this service to make it more streamline by removing some of the manual steps, make it easier to consume for the customer and also enable improvements to service reporting by improving the data points captured as part of this service. This project is flexible as there are many elements we can look at in isolation, such as: 1. Automation of initial engagement through self-service forms using the IT service management tool 2. Development of automated AI support chat bot to assist customers with common questions/support queries 3. Automation of part of the toolkit assessment, such as the Cyber Criticality Assessment of a new system/solution, using either a custom developed web form/app or Microsoft Forms. 4. Developing integration between the Uni IT Configuration Management Database (CMDB) and the Cyber Criticality Assessment (#3). This project is flexible and would be a fantastic learning opportunity for a group of students who are interested in Cyber Advisory, Architecture or Risk Management - it would allow them to learn skills in these domains while also applying their knowledge/expertise to optimise part of the service through creative automation. We have a number of team members who would be willing to work with a group of students to unpick the current processes and work collaboratively to identify creative ways and ideas to improve parts of the current service offering - we can make this as small or big as we want to. We would be happy to discuss this further if this project/challenge is something the students would be keen to explore.

Client: Goran Mitrovic

Location: UWA Crawley Campus Ken & Julie Michael Building 7 Fairway, Crawley WA 6009

IP: Open to further discussion about best IP options.

Cannellonni-protocol visualisation, monitoring and logging

CAN is a networking protocol similar to message-based UDP, but fit for automotive and industrial applications. For means of data collection (or non-critical or time-insensitive interactions) it can be beneficial …

CAN is a networking protocol similar to message-based UDP, but fit for automotive and industrial applications. For means of data collection (or non-critical or time-insensitive interactions) it can be beneficial to expose this via IP network tunnelling - the Cannelloni protocol is becoming a common way to do this. There are already various tools for tight integration of cannelloni data with the linux-only socketCAN driver, but I have need for tools to view and interact with this data by users who may not be hosted on linux and/or may not know details of handling socketCAN utilities. This project will require skills related to basic networking and GUI tools for displaying real-time data, it will also require gaining basic knowledge of some foundational CAN protocol standards (J1939, OBD-II, etc.) and common utilities/formats used with CAN (DBC files, candump logs, etc.), as this may affect how visualisation of the data is best achieved. Additionally integrating this project with pre-existing CAN and networking tools (such as wireshark and python-can) via plugin interfaces where possible will make the functionality more approachable and familiar to users. For a more discrete set of specific requirements: Handle bidirectional cannelloni communication and elevate instances of missing/unordered data to the awareness of the user, collate and display the raw CAN communication along with statistical information for the various CAN IDs, unpack the raw CAN data to engineering values (likely based on a DBC specification) where user defined parameters of interest can be filtered for and displayed as real-time data (and allow previously logged data to be viewed in a similar fashion), log raw and converted data to common file formats, and a GUI to pack real values into (DBC-specified) encoded can frames for transmission via the cannelloni protocol. The encoding and decoding of CAN data will require rigorous testing due to unique endianness and data types being used to transmit values. The GUI will need to be interactive in what CAN parameters it can display and how, where changes in analog, digital or enumerated states may need to be displayed. Specific opportunities of integration points with existing software are: implementing a cannelloni backend for python-can, implementing a wireshark cannelloni dissector, and handling logging formats including wireshark pcapng files and can-utils' candump logging format.

Client: Kevin Cope

Location: RCT Perth Head Office, Kewdale 6105

Multiple Teams

IP: Creative Commons (open source)

Multi-patient monitoring simulation - a research tool

Hospitals around the world are beginning to use remote patient monitoring systems to have clinicians monitor and interact with patients remotely. Benefits of such systems include timely and continuous monitoring, …

Hospitals around the world are beginning to use remote patient monitoring systems to have clinicians monitor and interact with patients remotely. Benefits of such systems include timely and continuous monitoring, early detection of deterioration, increased clinical efficiency, and many more. However, what impact does remote patient monitoring have on clinicians' work behaviour and ultimately their ability to care for patients? I am a human factors psychologist and am interested in using a remote patient monitoring simulation as a research tool to conduct behavioural experiments with clinicians to understand the cognitive demands (e.g. attention) placed on them by the system. In this project, I am looking to have a multi-patient monitoring simulation software built. The software will need to be able to: 1) read in data files of patients' physiological information (e.g. heart rate, oxygen saturation, blood pressure, etc.); 2) display the physiological information in a dynamic graphical user interface (GUI) environment; 3) record user interaction (e.g. screen activities, key presses, time stamps, etc.) with the GUI and output the record in analysable format (e.g. csv). Further details: the GUI environment needs to be able to 1) display multiple panels to represent multiple patients' health information; 2) have auditory output associated with the health information (e.g. pulse oximetry); 3) be configurable in terms of number of patient panels visible, on/off of auditory output, number of physiological vital signs to be displayed, etc. As an example, the GUI might look like the ones as shown in the image in this link: https://uknow.uky.edu/uk-healthcare/university-kentucky-chandler-hospital/new-central-monitoring-station-hold-open-house. Happy to discuss the level of fidelity of the simulation software with the team with respect of the project's scope.

Client: Simon Li

Location: UWA School of Psychological Science (UWA campus building #347 - main psychology building)

IP: Open to further discussion about best IP options.

Weightlifting Improvement Coefficient

At Small Goods Gym, we provide alternative competition formats in an attempt to allow clients and the greater community to celebrate and further engage in their training and progress. When …

At Small Goods Gym, we provide alternative competition formats in an attempt to allow clients and the greater community to celebrate and further engage in their training and progress. When it comes to charting progress, we believe in personal development over interpersonal comparison and feel interpersonal comparison stops many from engaging in these events. We want to create an “improvement coefficient” for this reason- with the coefficient aiming to generate useful information about client progress. Project goals: Look through current data on powerlifting and olympic weightlifting and determine factors related to performance (such as body weight, age, years trained or competed and more) Identify the gravity of each of these relative to progress Come up with an evidence-based formula to determine an “objective rate of progress” that could be applied to an incredibly broad array of clients independent of their differences. If the scope of this project is too small, we would appreciate the scope expanding into two key formulae: one that includes bodyweight and similar metrics and one that excludes this information (as many prospective competitors are not comfortable jumping on scales). We would also like a comparison of the relative accuracy of both formulae.

Client: Joel Mullen

Location: Morley

NDA Required

IP: Creative Commons (open source)

Produce images for teaching or publication

Excavation of human remains from archaeological or forensic contexts often requires disemmination of information in a respectful manner. Photographs are not always appropriate. We therefore require a way of accurately …

Develop an intuitive and field-ready software platform that enables efficient processing, organisation, and analysis of biological video data to support Polar BLAST’s scientific objectives

This software will serve as a versatile tool for researchers working in remote polar and deep-sea environments, integrating multiple data sources to enhance marine wildlife monitoring and conservation efforts. Key …

This software will serve as a versatile tool for researchers working in remote polar and deep-sea environments, integrating multiple data sources to enhance marine wildlife monitoring and conservation efforts. Key Goals & Features: Develop an online/offline application for tagging video data with standardised metadata. Enable automatic or user-driven renaming and sub-folder organisation of video files based on field events. Automatically extract and assign GPS location and timestamp from recorded video data where possible. Provide manual input fields for key parameters, including depth (m), temperature (°C), dive time, and deployment type. Generate dive profile reconstructions using real-time temperature and depth data from subsea technologies. Provide automated reports and analytics, summarising total video footage, depth statistics (min/med/max), and event logs. Include a pollution logging feature to document and track environmental disturbances. Allow users to capture reference images of organisms, linking them to taxonomic databases for streamlined identification. Integrate GPS mapping to visualise video and dive locations for spatial analysis. Ensure offline functionality for remote use. Provide an intuitive, efficient workflow for researchers conducting real-time data collection. By addressing these objectives, the software will streamline data management, enhance scientific analysis, and facilitate long-term biodiversity monitoring and conservation planning in some of the planet’s most fragile ecosystems.

Client: Paige Maroni

IP: Creative Commons (open source)

AI Learning Outcome Builder

This project involves building a web-based tool that assists users in drafting and refining learning outcomes for courses. The tool will guide users step-by-step with a series of conversational questions …

This project involves building a web-based tool that assists users in drafting and refining learning outcomes for courses. The tool will guide users step-by-step with a series of conversational questions to help them articulate outcomes that are specific, measurable, and aligned with Bloom’s Taxonomy. The AI will suggest suitable templates, action verbs, and sentence structures, ensuring the learning outcomes are well-formulated. Once the learning outcome is constructed, the tool will evaluate its quality and suggest improvements if necessary. Project Tasks Project requirements gathering and initial designing, defining the AI model and technologies to be used and defining the scope of input data (list of units/ courses and existing learning outcomes) AI Model Training & Integration UI/UX Design: Design a user-friendly interface that allows users to interact with the tool, answer dynamic questions, or input existing learning outcomes. Design a modification interface to let the user adjust and update the generated outcome. Development of other functionalities Develop AI based, Dynamic Question Flow Develop suggested templates and action verbs based on the type of outcome the user is creating, align with Bloom’s Taxonomy. Learning Outcome Generation and Evaluation

Client: Maryam Vettoor

Location: You can also contact [email protected] or [email protected]

IP: Creative Commons (open source)

Narrative Ethics

Development of a narrative ethics VR app for use by Health Humanities students and Health Care Professions students- eg Medicine. VR (or other digital technology) can be used to tell …

AI-mediated online interprofessional clinical case scenarios

This proposed project involves designing an online interprofessional clinical case scenario designed for digital interaction by students in both asynchronous format and AI-mediated discussion format. Interprofessional education (IPE) is essential …

This proposed project involves designing an online interprofessional clinical case scenario designed for digital interaction by students in both asynchronous format and AI-mediated discussion format. Interprofessional education (IPE) is essential for preparing healthcare students to collaborate effectively. With the rise of digital learning, asynchronous and AI-moderated case-based discussions offer scalable, flexible platforms for IPE. IPE is where students from different health disciplines (e.g. medicine, nursing, pharmacy, allied health) work together an a clinical case scenario to learn from, with and about each other's disciplines. Creating a structured digital interprofessional case scenario involves designing a realistic, engaging, and pedagogically sound clinical situation that encourages collaboration, critical thinking, and the expression of student agency. Students will be provided with the content of the case scenario and discipline-specific prompts for each of the health professions. The content will be delivered in the scenario in 3–4 unfolding parts (e.g. initial patient presentation, hospital stay, discharge planning) to simulate real-time decision-making and encourage ongoing engagement. Ideally, it should be designed for two delivery formats in Blackboard Ultra: Asynchronous Format (e.g. students use the discussion board with weekly case updates, prompt students to respond to the scenario and each other and include guiding questions (e.g., “What are the priorities for your profession at this stage?”) and AI-Mediated Format (Using an AI chatbot or structured prompts to simulate a facilitator and get AI to ask reflective questions, summarise key points, or challenge assumptions)

Client: Emma Bartle

Location: Department of Health Professions Education, The University of Western Australia

Multiple Teams

IP: Creative Commons (open source)

EduLead SimLab: Building a data-driven simulation platform for leadership training

We are inviting students in Data Engineering, Simulation Development, and AI Systems to collaborate on an innovative project at the intersection of immersive technology and leadership development. The goal is …

We are inviting students in Data Engineering, Simulation Development, and AI Systems to collaborate on an innovative project at the intersection of immersive technology and leadership development. The goal is to design and engineer an advanced simulation-based training platform to prepare aspiring educational leaders for real-world critical incidents. This system will generate and manage branching, interactive leadership scenarios, covering everything from crisis response and staff management to student welfare and policy implementation—all within a safe, simulated environment. Educational leadership involves complex, high-stakes decision-making. However, current training methods often lack dynamic, data-driven simulations that mirror the pressures and uncertainties leaders face. This project offers a chance to build a next-generation simulation system that: Models real-world educational leadership challenges as data-rich scenarios Uses decision trees, state management, and adaptive branching logic Integrates dynamic feedback loops (real-time indicators, outcome analytics, post-scenario debriefs) Embeds AI-assisted reflection tools to support iterative learning Captures user interaction data for longitudinal performance analysis

Client: Dr Wei Zhang

Location: Graduate School of Education

NDA Required

Multiple Teams

IP: Open to further discussion about best IP options.

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CITS3200 - Professional Computing

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Shadow IT discovery and attack surface detection for small businesses.

AI Unit Rule Builder

Automated HPTLC Database Screening for Compound Identification

Cyber Secure by Design

Cannellonni-protocol visualisation, monitoring and logging

Multi-patient monitoring simulation - a research tool

Weightlifting Improvement Coefficient

Produce images for teaching or publication

Develop an intuitive and field-ready software platform that enables efficient processing, organisation, and analysis of biological video data to support Polar BLAST’s scientific objectives

AI Learning Outcome Builder

Narrative Ethics

AI-mediated online interprofessional clinical case scenarios

EduLead SimLab: Building a data-driven simulation platform for leadership training

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