DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting th ....DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system. The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems. Read moreRead less
Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while pres ....Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while preserving the data privacy. These tools should provide significant benefits to the privacy of cloud users, as well as financial and reputation benefits to the IT industry, by significantly reducing the likelihood of massive user data privacy breaches in the event of a cyber-hacking attack on the cloud server.Read moreRead less
Privacy Preservation over 5G and IoT Smart Devices. This project aims to investigate privacy preservation protocols in a 5G integrated IoT environment through an analysis of the depth of smart-device use in common smart domains. 5G’s addition to IoT-based smart devices will be effectively deployed and utilised by a large majority of individual and organisation-based users. The knowledge-based ontology and tools developed in the project will help form the new privacy preservation mechanisms that ....Privacy Preservation over 5G and IoT Smart Devices. This project aims to investigate privacy preservation protocols in a 5G integrated IoT environment through an analysis of the depth of smart-device use in common smart domains. 5G’s addition to IoT-based smart devices will be effectively deployed and utilised by a large majority of individual and organisation-based users. The knowledge-based ontology and tools developed in the project will help form the new privacy preservation mechanisms that are required for the 5G enabled environment. The construction of new AI-based tools and testing facilities as well as the generation of new knowledge in the field of privacy preservation and collaboration between universities are expected outcomes of this project. Read moreRead less
Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for loca ....Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for localised IoT execution, through microservices executing autonomously on nodes of IoT fog networks. It will develop new techniques for automated discovery of microservices from enterprise systems and the verification of future-state system execution based on current-state behavioural and other properties such as security.Read moreRead less
Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes ....Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes are innovative technologies, guaranteeing accuracy and confidentiality of annotation results whilst protecting the privacy of data classification results. It enhances data-intensive outputs quality, which will benefit large data-intensive applications, such as cybersecurity protections via intrusion detection.Read moreRead less
Privacy-preserving online user matching. This project aims to develop efficient techniques to preserve the privacy of users of online matching websites used for finding employment, friends and partners. The project expects to generate new knowledge in privacy preserving user matching with multiple servers. The expected outcomes are new techniques that can find matching users without revealing their interests to the matching server and a prototype based on these techniques. This should alleviate ....Privacy-preserving online user matching. This project aims to develop efficient techniques to preserve the privacy of users of online matching websites used for finding employment, friends and partners. The project expects to generate new knowledge in privacy preserving user matching with multiple servers. The expected outcomes are new techniques that can find matching users without revealing their interests to the matching server and a prototype based on these techniques. This should alleviate the privacy concerns of people using online tools that require providing personal information.Read moreRead less
Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobil ....Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobile environments. This should protect the privacy of individuals and increase users’ trust in location-based systems.Read moreRead less
Energy big data analytics from a cybersecurity perspective. This project aims to develop a framework on energy big data analytics from security and privacy perspectives. Unlike other big data analytics such as social network big data analytics, energy big data analytics involve research challenges on how to cope with real-time tight cyber-physical couplings, and security/safety of the smart grid system. This project will develop advanced data-driven algorithms that are capable of detecting coord ....Energy big data analytics from a cybersecurity perspective. This project aims to develop a framework on energy big data analytics from security and privacy perspectives. Unlike other big data analytics such as social network big data analytics, energy big data analytics involve research challenges on how to cope with real-time tight cyber-physical couplings, and security/safety of the smart grid system. This project will develop advanced data-driven algorithms that are capable of detecting coordinated cyber-attacks that will potentially lead to catastrophic cascaded failures; and develop new solutions in detecting the false data-injection attacks that are conventionally considered as unobservable. This project will provide the benefit of enhancing our national critical infrastructure's security.Read moreRead less
Blockchain-Enabled Federated Learning for Secure and Decentralised Learning. This project aims to develop novel blockchain-enabled federated learning techniques for secure and decentralised learning. It addresses an important and urgent machine learning problem, that is, the data useful for training machine learning models are often held by different owners who are not willing to share their data due to privacy concerns, resulting in isolated data islands. The project will result in a set of inn ....Blockchain-Enabled Federated Learning for Secure and Decentralised Learning. This project aims to develop novel blockchain-enabled federated learning techniques for secure and decentralised learning. It addresses an important and urgent machine learning problem, that is, the data useful for training machine learning models are often held by different owners who are not willing to share their data due to privacy concerns, resulting in isolated data islands. The project will result in a set of innovative algorithms that provide solutions to the key challenges in blockchain-enabled federated learning. The expected outcomes of the project will dramatically advance the frontier of machine learning and blockchain research, and have massive social and economic benefits for Australia and international communities.Read moreRead less
Private searching on streaming data. This project aims to explore secure and practical solutions for various internet search types and to develop a prototype of a private searching system to avoid compromising user privacy whilst sharing query requests. Searching of streaming data allows collection of useful information from huge streaming sources of data such as on-line news feeds and internet chat-rooms. Current solutions for this problem are inefficient and restricted to a couple of simple se ....Private searching on streaming data. This project aims to explore secure and practical solutions for various internet search types and to develop a prototype of a private searching system to avoid compromising user privacy whilst sharing query requests. Searching of streaming data allows collection of useful information from huge streaming sources of data such as on-line news feeds and internet chat-rooms. Current solutions for this problem are inefficient and restricted to a couple of simple search types, and vulnerable to identifying the search requester. The project expects to develop private searching tools to protect the privacy of user's search queries. This will have the potential to detect any attacks to our digital infrastructure while keeping the search criteria classified, and could have applications in Australian counter-terrorism and homeland defence.Read moreRead less