ORCID Profile
0000-0002-1002-057X
Current Organisation
Massey University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
Date: 2012
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 12-2007
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 10-2006
DOI: 10.1109/EDOC.2006.16
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 08-2014
Publisher: Elsevier BV
Date: 06-2023
Publisher: Springer US
Date: 2003
Publisher: Elsevier BV
Date: 09-2022
Publisher: Springer International Publishing
Date: 2017
Publisher: Hawaii International Conference on System Sciences
Date: 2019
Publisher: Mary Ann Liebert Inc
Date: 05-2014
Abstract: Australians in rural and remote areas live with far poorer health outcomes than those in urban areas. Telehealth services have emerged as a promising solution to narrow this health gap, as they improve the level and ersity of health services delivery to rural and remote Australian communities. Although the benefits of telehealth services are well studied and understood, the uptake has been very slow. To understand the underpinning issues, we conducted a literature review on barriers to telehealth adoption in rural and remote Australian communities, based on the published works of Australian clinical trials and studies. This article presents our findings using a comprehensive barrier matrix. This matrix is composed of four stakeholders (governments, technology developers and providers, health professionals, and patients) and five different categorizations of barriers (regulatory, financial, cultural, technological, and workforce). We explain each cell of the matrix (four stakeholders×five categories) and map the reported work into the matrix. Several exemplary barrier cases are also described to give more insights into the complexity and dilemma of adopting telehealth services. Finally, we outline recent technological advancements that have a great potential to overcome some of the identified barriers.
Publisher: IEEE
Date: 07-2008
Publisher: Springer International Publishing
Date: 2018
Publisher: ICST
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE Comput. Soc
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-02-2023
Publisher: Springer Science and Business Media LLC
Date: 25-08-2011
Publisher: Elsevier BV
Date: 03-2020
Publisher: MDPI AG
Date: 16-08-2023
DOI: 10.3390/CRYPTOGRAPHY7030041
Abstract: In this paper, we propose a new symmetric stream cipher encryption algorithm based on Graph Walks and 2-dimensional matrices, called Matrix Encryption Walks (MEW). We offer ex le Key Matrices and show the efficiency of the proposed method, which operates in linear complexity with an extremely large key space and low-resource requirements. We also provide the Proof of Concept code for the encryption algorithm and a detailed analysis of the security of our proposed MEW. The MEW algorithm is designed for low-resource environments such as IoT or smart devices and is therefore intended to be simple in operation. The encryption, decryption, and key generation time, along with the bytes required to store the key, are all discussed, and similar proposed algorithms are examined and compared. We further discuss the avalanche effect, key space, frequency analysis, Shannon entropy, and chosen/known plaintext-ciphertext attacks, and how MEW remains robust against these attacks. We have also discussed the potential for future research into algorithms such as MEW, which make use of alternative structures and graphic methods for improving encryption models.
Publisher: Elsevier BV
Date: 10-2020
Publisher: IEEE
Date: 07-2007
DOI: 10.1109/ICWS.2007.70
Publisher: Association for Computing Machinery (ACM)
Date: 23-11-2022
DOI: 10.1145/3484945
Abstract: Multi-dimensional data anonymization approaches (e.g., Mondrian) ensure more fine-grained data privacy by providing a different anonymization strategy applied for each attribute. Many variations of multi-dimensional anonymization have been implemented on different distributed processing platforms (e.g., MapReduce, Spark) to take advantage of their scalability and parallelism supports. According to our critical analysis on overheads, either existing iteration-based or recursion-based approaches do not provide effective mechanisms for creating the optimal number of and relative size of resilient distributed datasets (RDDs), thus heavily suffer from performance overheads. To solve this issue, we propose a novel hybrid approach for effectively implementing a multi-dimensional data anonymization strategy (e.g., Mondrian) that is scalable and provides high-performance. Our hybrid approach provides a mechanism to create far fewer RDDs and smaller size partitions attached to each RDD than existing approaches. This optimal RDD creation and operations approach is critical for many multi-dimensional data anonymization applications that create tremendous execution complexity. The new mechanism in our proposed hybrid approach can dramatically reduce the critical overheads involved in re-computation cost, shuffle operations, message exchange, and cache management.
Publisher: Springer Science and Business Media LLC
Date: 14-03-2012
Publisher: IEEE
Date: 2010
Publisher: Association for Computing Machinery (ACM)
Date: 03-12-2023
DOI: 10.1145/3530809
Abstract: Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats that are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system needs to be developed that has the ability of monitoring and capturing various types of threats, analyzing, and devising a plan to avoid further attacks. This article provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, analysis of the techniques, and evaluation methods. Last, we highlight misconceptions, insights, and limitations of this study and suggest some future work directions to address the limitations.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: MDPI AG
Date: 26-05-2022
DOI: 10.3390/COMPUTERS11060085
Abstract: Existing generative adversarial networks (GANs), primarily used for creating fake image s les from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake s les that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.
Publisher: ICST
Date: 2013
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/9410278
Abstract: Most existing virtual machine introspection (VMI) technologies analyze the status of a target virtual machine under the assumption that the operating system (OS) version and kernel structure information are known at the hypervisor level. In this paper, we propose a model of virtual machine (VM) security monitoring based on memory introspection. Using a hardware-based approach to acquire the physical memory of the host machine in real time, the security of the host machine and VM can be diagnosed. Furthermore, a novel approach for VM memory forensics based on the virtual machine control structure (VMCS) is put forward. By analyzing the memory of the host machine, the running VMs can be detected and their high-level semantic information can be reconstructed. Then, malicious activity in the VMs can be identified in a timely manner. Moreover, by mutually analyzing the memory content of the host machine and VMs, VM escape may be detected. Compared with previous memory introspection technologies, our solution can automatically reconstruct the comprehensive running state of a target VM without any prior knowledge and is strongly resistant to attacks with high reliability. We developed a prototype system called the VEDefender. Experimental results indicate that our system can handle the VMs of mainstream Linux and Windows OS versions with high efficiency and does not influence the performance of the host machine and VMs.
Publisher: Springer Science and Business Media LLC
Date: 25-09-2016
Publisher: MDPI AG
Date: 03-03-2021
DOI: 10.3390/ELECTRONICS10050589
Abstract: Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g., top-down, bottom-up, and hybrid) have been implemented on the MapReduce platform to take advantage of scalability and parallelism. However, MapReduce inherent lack support for iteration intensive algorithm implementation such as subtree generalization. This paper proposes Distributed Dataset (RDD)-based implementation for a subtree-based data anonymization technique for Apache Spark to address the issues associated with MapReduce-based counterparts. We describe our RDDs-based approach that offers effective partition management, improved memory usage that uses cache for frequently referenced intermediate values, and enhanced iteration support. Our experimental results provide high performance compared to the existing state-of-the-art privacy preserving approaches and ensure data utility and privacy levels required for any competitive data anonymization techniques.
Publisher: Springer Science and Business Media LLC
Date: 18-11-2020
Publisher: MDPI AG
Date: 04-08-2021
DOI: 10.3390/S21165264
Abstract: Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 2009
Publisher: American Geophysical Union (AGU)
Date: 10-2019
DOI: 10.1029/2019GB006276
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/EUC.2010.125
Publisher: Wiley
Date: 25-02-2019
DOI: 10.1111/GCB.14537
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/EUC.2010.126
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 15-01-2018
Publisher: MDPI AG
Date: 22-12-2021
DOI: 10.3390/S22010032
Abstract: A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.
Publisher: Springer Singapore
Date: 2017
Publisher: Springer International Publishing
Date: 2020
Publisher: MDPI AG
Date: 15-08-2023
DOI: 10.3390/ELECTRONICS12163463
Abstract: The translation of traffic flow data into images for the purposes of classification in machine learning tasks has been extensively explored in recent years. However, the method of translation has a significant impact on the success of such attempts. In 2019, a method called DeepInsight was developed to translate genetic information into images. It was then adopted in 2021 for the purpose of translating network traffic into images, allowing the retention of semantic data about the relationships between features, in a model called MAGNETO. In this paper, we explore and extend this research, using the MAGNETO algorithm on three new intrusion detection datasets—CICDDoS2019, 5G-NIDD, and BOT-IoT—and also extend this method into the realm of multiclass classification tasks using first a One versus Rest model, followed by a full multiclass classification task, using multiple new classifiers for comparison against the CNNs implemented by the original MAGNETO model. We have also undertaken comparative experiments on the original MAGNETO datasets, CICIDS17, KDD99, and UNSW-NB15, as well as a comparison for other state-of-the-art models using the NSL-KDD dataset. The results show that the MAGNETO algorithm and the DeepInsight translation method, without the use of data augmentation, offer a significant boost to accuracy when classifying network traffic data. Our research also shows the effectiveness of Decision Tree and Random Forest classifiers on this type of data. Further research into the potential for real-time execution is needed to explore the possibilities for extending this method of translation into real-world scenarios.
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11914853_26
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Mary Ann Liebert Inc
Date: 04-2014
Abstract: Evaluating telehealth programs is a challenging task, yet it is the most sensible first step when embarking on a telehealth study. How can we frame and report on telehealth studies? What are the health services elements to select based on the application needs? What are the appropriate terms to use to refer to such elements? Various frameworks have been proposed in the literature to answer these questions, and each framework is defined by a set of properties covering different aspects of telehealth systems. The most common properties include application, technology, and functionality. With the proliferation of telehealth, it is important not only to understand these properties, but also to define new properties to account for a wider range of context of use and evaluation outcomes. This article presents a comprehensive framework for delivery design, implementation, and evaluation of telehealth services. We first survey existing frameworks proposed in the literature and then present our proposed comprehensive multidimensional framework for telehealth. Six key dimensions of the proposed framework include health domains, health services, delivery technologies, communication infrastructure, environment setting, and socioeconomic analysis. We define a set of ex le properties for each dimension. We then demonstrate how we have used our framework to evaluate telehealth programs in rural and remote Australia. A few major international studies have been also mapped to demonstrate the feasibility of the framework. The key characteristics of the framework are as follows: (a) loosely coupled and hence easy to use, (b) provides a basis for describing a wide range of telehealth programs, and (c) extensible to future developments and needs.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 14-06-2023
DOI: 10.3390/FI15060214
Abstract: Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware s les are present in the training dataset, resulting in high false-positive rates. To address this issue, we propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against the presence of malware variants affected by such control flow obfuscation techniques. Using the average entropy features of each malware family as inputs, in addition to the image features, our model generates the parameters for the feature layers, to more accurately adjust the feature embedding for different malware families, each of which has obfuscated malware variants. In addition, our proposed method can classify malware classes, even if there are only one or a few training s les available. Our model utilizes few-shot learning with the extracted features of a pre-trained network (e.g., VGG-16), to avoid the bias typically associated with a model trained with a limited number of training s les. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware s les that belong to the same malware family, even in the presence of obfuscated malware variants. Our experimental results, validated by N-way on N-shot learning, show that our model is highly effective in classification accuracy, exceeding a rate %, compared to other similar methods.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11575771_6
Publisher: MDPI AG
Date: 11-03-2022
DOI: 10.3390/COMPUTERS11030041
Abstract: The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of the network intrusion detection method, which combines a Q-learning based reinforcement learning with a deep feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor, which is set as 0.001 under 250 episodes of training, yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.
Publisher: MDPI AG
Date: 20-10-2020
DOI: 10.3390/ELECTRONICS9101732
Abstract: Recent studies in data anonymization techniques have primarily focused on MapReduce. However, these existing MapReduce based approaches often suffer from many performance overheads due to their inappropriate use of data allocation, expensive disk I/O access and network transfer, and no support for iterative tasks. We propose “SparkDA” which is a new novel anonymization technique that is designed to take the full advantage of Spark platform to generate privacy-preserving anonymized dataset in the most efficient way possible. Our proposal offers a better partition control, in-memory operation and cache management for iterative operations that are heavily utilised for data anonymization processing. Our proposal is based on Spark’s Resilient Distributed Dataset (RDD) with two critical operations of RDD, such as FlatMapRDD and ReduceByKeyRDD, respectively. The experimental results demonstrate that our proposal outperforms the existing approaches in terms of performance and scalability while maintaining high data privacy and utility levels. This illustrates that our proposal is capable to be used in a wider big data applications that demands privacy.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 10-2023
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 08-2018
No related grants have been discovered for Julian Jang-Jaccard.