ORCID Profile
0000-0002-3748-0277
Current Organisation
Federation University Australia
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Epidemiology | Mental Health | Public Health and Health Services | Health Care Administration |
Health Policy Economic Outcomes | Health Policy Evaluation | Mental Health Services
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 06-2006
DOI: 10.1007/BF03178892
Publisher: Elsevier BV
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11573067_36
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11573067_38
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 10-2011
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 09-2012
Publisher: MDPI AG
Date: 12-01-2022
DOI: 10.3390/ELECTRONICS11020238
Abstract: The development of cyber-assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2007
Publisher: IEEE
Date: 16-12-2020
Publisher: IEEE
Date: 16-12-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 06-2020
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 08-2012
DOI: 10.1109/NCA.2012.39
Publisher: IEEE
Date: 11-2009
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 02-2023
DOI: 10.1186/S42400-022-00136-7
Abstract: Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA h er their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment.
Publisher: IEEE
Date: 08-2011
DOI: 10.1109/NCA.2011.63
Publisher: IEEE
Date: 04-2010
Publisher: Springer Science and Business Media LLC
Date: 04-08-2018
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2006
Publisher: IEEE
Date: 08-2011
DOI: 10.1109/NCA.2011.62
Publisher: Elsevier BV
Date: 11-1998
Publisher: IEEE
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
DOI: 10.1109/TMC.2013.78
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 2010
Publisher: IEEE
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Academy Publisher
Date: 12-2012
Publisher: IEEE
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 06-09-2020
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 08-2016
Publisher: ACM
Date: 03-11-2013
Publisher: Elsevier BV
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2007
Publisher: MDPI AG
Date: 09-2023
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 08-2019
Publisher: Elsevier BV
Date: 09-2018
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 12-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 02-2014
Publisher: IEEE
Date: 09-2015
Publisher: Queensland University of Technology
Date: 2023
DOI: 10.5204/REP.EPRINTS.241144
Abstract: Overview of the Project Gender inclusivity and equal employment opportunities are key priorities for the Victorian Government. The Gender Equality Act 2020 (the Act) commenced in March 2021 and laid the foundation to improve workplace gender equality in the Victorian public sector. The legislation requires Victorian public sector entities to explicitly address intersecting forms of inequality and disadvantage. The research project aimed to centre the voices of women with disability to provide evidence-based insights into the enablers, barriers and inclusive practices shaping their career progression and promotion in the Victorian Public Service. The research team reviewed scholarly literature, analysed data extracts from the People Matter Survey (2021) and interviewed 49 women with disability from across the Victorian Public Service. Summary of Key Findings People Matter Survey Data 2021 Analysis of the People Matter Survey 2021 data extracts identified statistically significant insights. People who identified as having a disability analysed by gender identity indicated that: ● women and people who identified as non-binary and ‘other’ reported having a disability more often than men. ● women were more likely to use one or more flexible work arrangements. ● more requests for workplace adjustments were made by women, non-binary or ‘other’ gender identities and disability was often identified as a reason for requesting workplace adjustments. ● women and men reported low perceptions of workplace culture related to disability. This was significantly lower for respondents who identified as non-binary, ‘other’ or who preferred not to state their gender. Research Interviews with Women with Disability Interviews with women with disability identified three career patterns. Firstly, broadly inclusive, and positive career experiences. Secondly, broadly non-inclusive career experiences which led participants to feel unsure they had a future career in the VPS. Thirdly, most participants experienced a range of inclusive and non-inclusive career experiences which varied depending on the VPS employer or team in which they were employed. Overall, participants highlighted a desire for: ● the VPS to move forward with more consistency in how it enables the careers of women with disability across all roles and levels of seniority. ● the VPS to move away from putting women with disability in the ‘too hard basket’ towards developing a culture where disability inclusion is characterised by relationships and interactions that reflect ‘respect’ and ‘trust’. Eight themes draw together insights from the interviews with women with disability and identify experiences of the VPS workplace that can enable or create barriers to career progression: ● Sharing Disability Information ● Requesting Workplace Adjustments ● Disability Advocacy ● Team Relations ● Impact of Managers and Supervisors ● Mentorship ● Disability Leadership ● Policy Context and Application To build on the enabling aspects of women with disabilities experiences and remove barriers, the VPS should focus on fostering VPS workplaces where respect and trust are embedded throughout the broader culture. There may be value in identifying one or a small group of VPS employers to lead on developing the inclusive practices identified by participants. The inclusive practices identified by participants were drawn together into three key areas: VPS Managers and Supervisors Psychological Safety and VPS Policies and Practices. Respecting the agency of women with disability, their capability and capacity to navigate their career contexts, the report suggests three key areas women with disability may want to focus their energy and sources of support: seeking out mentoring opportunities, considering how they can advocate for their inclusion requirements, and exploring opportunities to share their career experiences with other women with disability.
Publisher: Elsevier BV
Date: 04-2012
Publisher: IEEE
Date: 10-2011
Publisher: MDPI AG
Date: 23-10-2019
DOI: 10.3390/ELECTRONICS8111210
Abstract: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and erse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/AFIN.2010.21
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Elsevier BV
Date: 05-2006
Publisher: IEEE
Date: 12-2010
Publisher: MDPI AG
Date: 12-09-2020
DOI: 10.3390/ELECTRONICS9091500
Abstract: Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of t ered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are ided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training s les.
Publisher: Inderscience Publishers
Date: 2009
DOI: 10.1504/IJDMB.2009.023884
Abstract: Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued 'optimal docking area' ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.
Publisher: IEEE
Date: 2011
Publisher: IEEE
Date: 04-2009
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 04-2010
Publisher: SAGE Publications
Date: 07-03-2012
Abstract: Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-2154-9.CH019
Abstract: With the rapid expansion of digital media and the advancement of the artificial intelligence, robotics has drawn the attention of cyber security research community. Robotics systems use many Internet of Things (IoT) devices, web interface, internal and external wireless sensor networks and cellular networks for better communication and smart services. In iduals, industries and governments organisations are facing financial loses, losing time and sensitive data due these cyber attacks. The use these different devices and networks in robotics systems are creating new vulnerabilities and potential risk for cyber attacks. This chapter discusses about the possible cyber attacks and economics losses due to these attacks in robotics systems. In this chapter, we analyse the increasing uses of public and private robots, which has created possibility of having more cyber-crimes. Finally, contemporary and important mitigation approaches for these cyber attacks in robotic systems have been discussed in this chapter.
Publisher: IEEE
Date: 2011
Publisher: IGI Global
Date: 2007
DOI: 10.4018/978-1-59140-766-9.CH011
Abstract: Security and privacy protection are very strong requirements for the widespread deployment of wireless technologies for commercial applications. The primary aim of this chapter is to present an overview of the security and privacy issues by highlighting the need to secure access to wireless networks and the loss that might accrue from the breach of a network. The vulnerabilities of the IEEE 802.11 and Bluetooth networks are discussed, and a paradigm for secure wireless network is presented. The legal framework guiding the privacy issues in wireless communications is also presented.
Publisher: IGI Global
Date: 2006
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 04-2012
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 09-2015
DOI: 10.1109/NCA.2015.16
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Elsevier BV
Date: 09-2021
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-848-2.CH001
Abstract: Artificial neural network (ANN) is one of the main constituents of the artificial intelligence techniques. Like in many other areas, ANN has made a significant mark in the domain of healthcare applications. In this chapter, we provide an overview of the basics of neural networks, their operation, major architectures that are widely employed for modeling the input-to-output relations, and the commonly used learning algorithms for training the neural network models. Subsequently, we briefly outline some of the major application areas of neural networks for the improvement and well being of human health.
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11760191_97
Publisher: IEEE
Date: 03-2017
Publisher: IEEE
Date: 08-2014
DOI: 10.1109/NCA.2014.41
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 09-2008
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-848-2.CH010
Abstract: This chapter provides an overview of artificial neural network applications for the detection and classification of various gaits based on their typical characteristics. Gait analysis is routinely used for detecting abnormality in the lower limbs and also for evaluating the progress of various treatments. Neural networks have been shown to perform better compared to statistical techniques in some gait classification tasks. Various studies undertaken in this area are discussed with a particular focus on neural network’s potential in gait diagnostics. Ex les are presented to demonstrate the suitability of neural networks for automated recognition of gait changes due to aging from their respective gait patterns and their potential for identification of at-risk or non-functional gait.
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 12-2013
Publisher: IEEE
Date: 2005
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 12-2016
Publisher: MDPI AG
Date: 14-09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2011
DOI: 10.1109/TMC.2010.238
Publisher: Elsevier BV
Date: 07-2022
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-670-9.CH008
Abstract: In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present an overview of neural network-based forecasting models for foreign currency exchange (forex) rates. To demonstrate the suitability of neural network in forex forecasting, a case study on the forex rates of six different currencies against the Australian dollar is presented. We used three different learning algorithms in this case study, and a comparison based on several performance metrics and trading profitability is provided. Future research direction for enhancement of neural network models is also discussed.
Publisher: Elsevier BV
Date: 2008
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.825
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1616-5.CH012
Abstract: Object analysis using visual sensors is one of the most important and challenging issues in computer vision research due principally to difficulties in object representation, segmentation, and recognition within a general framework. This has motivated researchers to investigate exploiting the potential identification capability of RFID (radio frequency identification) technology for object analysis. RFID however, has a number of fundamental limitations including a short sensing range, missing tag detection, not working for all objects, and some items being just too small to be tagged. This has meant applying RFID alone has not been entirely effective in computer vision applications. To address these restrictions, object analysis approaches based on a combination of visual sensors and RFID have recently been successfully introduced. This chapter presents a contemporary review on these object analysis techniques for localisation, tracking, and object and activity recognition, together with some future research directions in this burgeoning field.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1616-5.CH013
Abstract: Radio Frequency Identification (RFID) systems and Wireless Sensor Networks (WSNs) are believed to be the two most important technologies in realizing the ubiquitous computing vision of Future Internet. RFID technology provides much cheaper solution for object identification and tracking based on radio wave. On the other hand, data on various parameters about the physical environment can be acquired using WSNs. Integration of the advantages of both RFID systems and WSNs would benefit many application domains. In RFID system, either an active RFID tag itself or an RFID reader (reading passive or semi-passive tags) consisting of an RF transceiver poses communication capability similar to that for nodes in WSNs. Therefore, instead of using single hop RFID protocol, RFID networks can take advantage of WSN-like multihop communication, and in this regard a number of WSN protocols can be useful for such RFID systems. In this chapter we present possible scenario of the integration of RFID system and WSNs and study a number of wireless sensor network protocols suitable to use in RFID system.
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.58
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.57
Publisher: Springer Science and Business Media LLC
Date: 21-04-2020
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.59
Publisher: Springer Science and Business Media LLC
Date: 04-07-2020
Publisher: IEEE
Date: 10-2015
Publisher: Elsevier BV
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-670-9.CH001
Abstract: The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.
Publisher: Elsevier BV
Date: 2014
Publisher: IEEE
Date: 05-2011
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-3994-2.CH073
Abstract: Object analysis using visual sensors is one of the most important and challenging issues in computer vision research due principally to difficulties in object representation, segmentation, and recognition within a general framework. This has motivated researchers to investigate exploiting the potential identification capability of RFID (radio frequency identification) technology for object analysis. RFID however, has a number of fundamental limitations including a short sensing range, missing tag detection, not working for all objects, and some items being just too small to be tagged. This has meant applying RFID alone has not been entirely effective in computer vision applications. To address these restrictions, object analysis approaches based on a combination of visual sensors and RFID have recently been successfully introduced. This chapter presents a contemporary review on these object analysis techniques for localisation, tracking, and object and activity recognition, together with some future research directions in this burgeoning field.
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 08-2013
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 06-2015
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 12-10-2020
Publisher: Nutrition Society of Malaysia
Date: 20-12-2021
Abstract: Introduction: Diabetes poses a heavy economic burden in Sri Lanka. High glycaemic index (GI) diets are known to promote a higher risk of diabetes. This study was aimed to determine the GI values of nine improved and three traditional rice varieties of Sri Lanka including Bg406, H.H.Z.36, Ld368, Bw367, Bg94-1, At405, At362, Bg300, Bg352, Sudu heenati, Madathawalu, and Pachchaperumal. Furthermore, comparisons of GI values between improved and traditional varieties, as well as the effect of subject gender and colour of pericarp on GI were described. Methods: Fourteen healthy subjects consisting of seven males and seven females were fed with a reference food and cooked rice varieties containing 50 g available carbohydrate GI were calculated. Results: The GI of 12 rice varieties varied from 40-69. All traditional varieties including Sudu heenati, Madathawalu and Pachchaperumal were in the low GI category presenting GI values of 51, 54, and 41, respectively. Rice with red pericarp obtained significantly lower GI compared to those with white pericarp. Yet, GI values obtained in males were significantly higher than females. Conclusion: The result of this study suggested that all traditional varieties and improved rice H.H.Z 36, Ld368, and Bg406 could have beneficial effects on lowering the glycaemic response in healthy subjects. Glycaemic index can be predicted from the colour of the rice grain. Gender should be considered in the determination of GI.
Publisher: Springer Science and Business Media LLC
Date: 20-08-2018
Publisher: Elsevier BV
Date: 09-2020
Publisher: MDPI AG
Date: 14-12-2020
Abstract: In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.
Publisher: IEEE
Date: 04-2010
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer International Publishing
Date: 2018
Publisher: Bentham Science Publishers Ltd.
Date: 09-03-2015
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.801
Publisher: SAGE Publications
Date: 25-05-2023
DOI: 10.1177/09500170231173586
Abstract: The rapidly expanding gig economy has been criticized for creating precarious and indecent working conditions. These critiques draw on decent work debates centred on employment classification, regulation and platform fairness, with less focus on the interactions between workers, platforms and clients, which are central to the experience of platform-mediated work. This article adopts a worker-centric relational perspective to explore decent work in the gig economy. Drawing on the experiences of workers in platform-mediated domestic care work, the insights from this study highlight the importance of social interactions and relationships, using an ethics of care lens, to elucidate how relational aspects shape workers’ experiences. The findings reveal platform workers centre mutuality of interests, responsiveness and reciprocity, attentiveness and solidarity to maintain a balance of care (care-for-self and care-for-others) when negotiating platform-mediated care work. This article contributes relationality as a key dimension of decent work currently overlooked in studies exploring gig work arrangements.
Publisher: Institute of Electrical Engineers of Japan (IEE Japan)
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-0004
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Informa UK Limited
Date: 2012
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 2004
Publisher: Springer Science and Business Media LLC
Date: 13-04-2016
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Institute of Electrical Engineers of Japan (IEE Japan)
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 07-2007
DOI: 10.1109/ICIS.2007.52
Publisher: MDPI AG
Date: 30-08-2021
DOI: 10.3390/ELECTRONICS10172103
Abstract: This paper proposes a new hybrid orthogonal frequency ision multiplexing (OFDM) form termed as DC-biased pulse litude modulated optical OFDM (DPO-OFDM) by combining the ideas of the existing DC-biased optical OFDM (DCO-OFDM) and pulse litude modulated discrete multitone (PAM-DMT). The analysis indicates that the required DC-bias for DPO-OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO-OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO-OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO-OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the in idual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO-OFDM component of DPO-OFDM for a given constellation size of PAM-DMT. With the component switching and the machine learning algorithms, DPO-OFDM-based LiFi is power efficient at a wide dimming range.
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 03-2013
Publisher: IEEE
Date: 11-2019
Publisher: Elsevier BV
Date: 10-2009
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 02-2017
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 12-2010
Publisher: MDPI AG
Date: 17-01-2020
DOI: 10.3390/ELECTRONICS9010173
Abstract: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.
Publisher: IEEE
Date: 07-2012
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: Elsevier BV
Date: 03-2005
DOI: 10.1016/J.JBIOMECH.2004.05.002
Abstract: This paper investigated application of a machine learning approach (Support vector machine, SVM) for the automatic recognition of gait changes due to ageing using three types of gait measures: basic temporal/spatial, kinetic and kinematic. The gaits of 12 young and 12 elderly participants were recorded and analysed using a synchronized PEAK motion analysis system and a force platform during normal walking. Altogether, 24 gait features describing the three types of gait characteristics were extracted for developing gait recognition models and later testing of generalization performance. Test results indicated an overall accuracy of 91.7% by the SVM in its capacity to distinguish the two gait patterns. The classification ability of the SVM was found to be unaffected across six kernel functions (linear, polynomial, radial basis, exponential radial basis, multi-layer perceptron and spline). Gait recognition rate improved when features were selected from different gait data type. A feature selection algorithm demonstrated that as little as three gait features, one selected from each data type, could effectively distinguish the age groups with 100% accuracy. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 10-2013
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 07-2013
Publisher: IEEE
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: IEEE
Date: 1993
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: MDPI AG
Date: 26-07-2021
DOI: 10.3390/ELECTRONICS10151783
Abstract: State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation.
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-2080-3.CH002
Abstract: Security and privacy protection are very critical requirements for the widespread deployment of RFID technologies for commercial applications. In this chapter, the authors first present the security and privacy requirement of any commercial system, and then highlight the security and privacy threats that are unique to an RFID system. The security and privacy preserving protocols for RFID system proposed in literature are elaborately discussed, analyzing their strengths, vulnerabilities, and implementation issues. The open research challenges that need further investigation, especially with the rapid introduction of erse RFID applications, are also presented.
Publisher: IEEE
Date: 09-2006
Publisher: Elsevier BV
Date: 11-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 09-2009
Publisher: MDPI AG
Date: 27-06-2023
DOI: 10.3390/COMPUTERS12070131
Abstract: Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be erted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation.
Publisher: Elsevier BV
Date: 11-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2006
Publisher: Elsevier BV
Date: 2011
Publisher: Elsevier BV
Date: 11-2014
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 09-2006
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 2005
Publisher: Springer Science and Business Media LLC
Date: 22-05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Location: Bangladesh
Start Date: 2018
End Date: 2021
Funder: Commonwealth Scientific and Industrial Research Organisation
View Funded ActivityStart Date: 2011
End Date: 2015
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2012
End Date: 12-2015
Amount: $184,000.00
Funder: Australian Research Council
View Funded Activity