Detecting Firmware Vulnerabilities in Smart Home Devices. 83% of Australians have smart home devices. 47% claim they have three or more. These devices are easily targeted by cyber-attacks, and searching for their vulnerabilities has become more crucial than ever. Our industry partner GPG is actively looking for ways to detect vulnerabilities in their smart home products, but have not found any existing methods that satisfy three critical requirements: 1) massive search, 2) cross platform detecti ....Detecting Firmware Vulnerabilities in Smart Home Devices. 83% of Australians have smart home devices. 47% claim they have three or more. These devices are easily targeted by cyber-attacks, and searching for their vulnerabilities has become more crucial than ever. Our industry partner GPG is actively looking for ways to detect vulnerabilities in their smart home products, but have not found any existing methods that satisfy three critical requirements: 1) massive search, 2) cross platform detection, and 3) finding unseen vulnerabilities. We therefore propose to use a series of new techniques such as efficient in-memory fuzzing, conditional formulas, and transfer learning to solve the above challenges. The project outcomes will help Australia gain cutting edge techniques in vulnerability detection. Read moreRead less
Risk-aware business process management. Risk-aware business process management will revolutionise the identification and treatment of risks in business processes by integrating the latest technologies for risk management and process management. It will provide organisations with a range of new tools and techniques for designing, deploying and monitoring risk-aware business processes.
Cost-aware business process management. The project aims to inform business process management (BPM) with the latest insights from the field of management accounting in order to make BPM systems cost-aware. By incorporating the cost dimension, organisations can obtain an accurate and immediate overview of the true cost of their processes and make cost-informed decisions.
Discovery Early Career Researcher Award - Grant ID: DE200101465
Funder
Australian Research Council
Funding Amount
$419,498.00
Summary
Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods emp ....Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods empowering a Web-based observatory dashboard of fake news propagation. This achieves significant benefits for media organisations, governments, the public, and academia via timely alerts, data-journalism reports, and novel data visualisations of social media landscape to distinguish between legitimate and deceptive contents.Read moreRead less
Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-ti ....Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.Read moreRead less
Location-aware Frequent Pattern Mining from Uncertain Spatial Transaction Data. Traditional transaction data mining exploits transactions without location information. Technological advances like GPS-enabled devices or radar sensors now allow the collection of transactions with spatial information. This project will address inherent data uncertainty by developing technical solutions to mine uncertain spatial transactions, and initiate a new research direction, location-aware frequent pattern min ....Location-aware Frequent Pattern Mining from Uncertain Spatial Transaction Data. Traditional transaction data mining exploits transactions without location information. Technological advances like GPS-enabled devices or radar sensors now allow the collection of transactions with spatial information. This project will address inherent data uncertainty by developing technical solutions to mine uncertain spatial transactions, and initiate a new research direction, location-aware frequent pattern mining. It will deliver theoretical foundations and technology for knowledge discovery from uncertain spatial transaction data and expand research horizons. Outcomes will provide social and economic benefits, for example, significantly improved analysis and detection of crime, weather diagnosis, and market basket analysis for business. Read moreRead less
Large-scale spatio-temporal data hashing for efficient data analytics. This project aims to systematically investigate the challenging problem of hash function learning for large-scale spatio-temporal data. The project will generate high quality hash codes for spatio-temporal data objects, enabling efficient similarity computation and thus supporting various data mining tasks and applications. The project expects to devise a series of learning frameworks by addressing the specific challenges of ....Large-scale spatio-temporal data hashing for efficient data analytics. This project aims to systematically investigate the challenging problem of hash function learning for large-scale spatio-temporal data. The project will generate high quality hash codes for spatio-temporal data objects, enabling efficient similarity computation and thus supporting various data mining tasks and applications. The project expects to devise a series of learning frameworks by addressing the specific challenges of different types of spatio-temporal data, such as univariate and multivariate. The project will provide significant benefits to both academia and industry in massive spatio-temporal data analysing, for example traffic flow mining and optimisation, weather forecasting and financial fraud detection.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
Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommen ....Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommendations for restructuring suitable parts as microservices. These microservices manage individual business objects via sets of lightweight distributed computational operations. The outcomes will support progressive evolution of an enterprise system, into distributed microservices running in public clouds, while still being integrated with "backend" systems.Read moreRead less
Big data fast response: real-time classification of big data stream. This project will provide a big data stream based data mining framework to build a real-time monitoring and decision making platform for business and government big data. By clever use of smart information, this breakthrough science will provide frontier technologies for managing and using huge data for social and economic benefits globally.