Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Towards High-performance and Fault-tolerant Distributed Java Implementations. Java Virtual Machines form an important part of the web and business
server market. Distributed Java Virtual Machines have the potential to
make a significant contribution to industries that utilize this
technology. An attractive platform for this purpose is the
cluster, a highly cost-effective and scalable parallel computer
model. However, realizing on such a platform a high performance virtual
machine implem ....Towards High-performance and Fault-tolerant Distributed Java Implementations. Java Virtual Machines form an important part of the web and business
server market. Distributed Java Virtual Machines have the potential to
make a significant contribution to industries that utilize this
technology. An attractive platform for this purpose is the
cluster, a highly cost-effective and scalable parallel computer
model. However, realizing on such a platform a high performance virtual
machine implementation tolerant to hardware and software faults, and
having efficient memory utilization, presents many challenging research
issues. This project will address these issues by extending a highly
efficient and extensible Java implementation to be aware of its cluster
environment.
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MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) ne ....MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) new methods for susceptibility diagnosis, 3) new defences that leverage privacy and utility. Data-oriented services are estimated to be valuable assets in the future. These techniques can help Australia gain cutting edge advantage in machine learning security and privacy and protect its intellectual property on these services.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they in ....Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they influence sample complexity. All the problems we focus on are not only important from the Machine Learning point of view, but are intriguing in their theoretical implications.Read moreRead less
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
Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.
Dynamic Cooperative Performance Optimizations. This project seeks to improve the reliability, security, and
performance of modern software systems. Security is a problem of such
scale that outbreaks of computer viruses etc. headline in major
financial newspapers. We approach the problem by addressing the key
performance problems that hold back the programming languagues widely
used for secure and reliable systems. By improving the reliability,
security and performance of computer system ....Dynamic Cooperative Performance Optimizations. This project seeks to improve the reliability, security, and
performance of modern software systems. Security is a problem of such
scale that outbreaks of computer viruses etc. headline in major
financial newspapers. We approach the problem by addressing the key
performance problems that hold back the programming languagues widely
used for secure and reliable systems. By improving the reliability,
security and performance of computer systems, this project will help
alleviate the millions of hours and dollars lost to inadvertent errors
and malicious software attacks. The project will give Australia an
international presence in a research area of great academic and
commercial importance.Read moreRead less
Privacy-preserving Biometrics based Authentication and Security. Password based authentication systems cannot verify genuine users. Biometric authentication can address this issue. However, the booming IoT applications and cloud computing require that the biometric authentication must be conducted in the privacy-protected setting in order to comply with privacy protection legal regulations. Latest reports show that current biometric authentication systems, under protected setting, exhibit poor ....Privacy-preserving Biometrics based Authentication and Security. Password based authentication systems cannot verify genuine users. Biometric authentication can address this issue. However, the booming IoT applications and cloud computing require that the biometric authentication must be conducted in the privacy-protected setting in order to comply with privacy protection legal regulations. Latest reports show that current biometric authentication systems, under protected setting, exhibit poor authentication performance, which is not commercially applicable. This project aims to investigate innovative solutions to this issue. The intended deliverables will include deep learning based biometric feature extractor, cancellable biometrics and cloud oriented biometrics security protocols. Read moreRead less
Improving Modern Programming Language Performance: A Memory-Conscious Approach. The performance of modern programming languages such as Java and C# lags that of imperative languages such as C and Fortran. A significant source of the performance gap is poor memory behavior, which future computer architectures will exacerbate. This project addresses the problem of poor memory behavior in modern programming languages such as Java and C# through an integrated attack that incorporates new garbage c ....Improving Modern Programming Language Performance: A Memory-Conscious Approach. The performance of modern programming languages such as Java and C# lags that of imperative languages such as C and Fortran. A significant source of the performance gap is poor memory behavior, which future computer architectures will exacerbate. This project addresses the problem of poor memory behavior in modern programming languages such as Java and C# through an integrated attack that incorporates new garbage collection algorithms, run-time techniques that optimize running programs, and new compiler analyses with both static and dynamic optimizations. The project will give Australia an
international presence in a research area of great academic and commercial importance.
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