Description:
Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
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Proceedings of the IEEE International Conference on Industrial Technology
Description:
Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
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Proceedings of the IEEE International Conference on Industrial Technology
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The aim of this study is to critically examine the development of corporate social responsibility (CSR) reporting through storytelling and institutionalism to understand how BHP Billiton Ltd. (BHP) has shaped stakeholders’ sensemaking of CSR and its CSR performance. To explore this, the study focuses on how the narrative themes in CSR reports by BHP corroborate with newspaper articles published by the Sydney Morning Herald (SMH) from 1992 until 2017. Results have identified instances where BHP used CSR storytelling to manage negative impressions of its CSR conduct, restore trust and re-establish legitimacy. This reinforces literature that identifies how companies use organisational storytelling to shape stakeholders understanding of social responsibility and manage impressions about CSR conduct.
Description:
Intentional Controlled Islanding (ICI) is the last protective measure that must be adopted in power systems to prevent forming unstable islands with load-generation imbalance power. If conventional protective schemes fail to save the power system against wide-area blackout, some cutsets are selected to keep the system operating while feeding essential loads. This paper investigates the effect of the number of partitions on the dynamic performance of controlled islanding and proposes a unified algorithm to identify the coherent generators, optimal cutsets, and timing of islanding in online applications. The algorithm provides an islanding solution with minimum power flow disruption while each island contains coherent groups of generators. The concept of Centre of Inertia (COI) referred angles of generators has been used to determine the critical timing of islanding. Different scenarios in NE 39-bus and IEEE 118-bus test systems were simulated in PowerFactory that led to instability in the power system, and then controlled islanding schemes with a different number of partitions were applied to create stable islands. To better demonstrate the shortcomings of the existing clustering techniques, the effect of the number of required islands on transient stability of controlled islanding and the necessity of devising an automatic method to recognise the number of islands is discussed.
Description:
In the era of technological change, the success of capital expenditure projects is significantly influenced by good project management, including project management structure. This paper presents how the roles of functional and project managers can positively influence project performance in a matrix structure. The focus on organizational structure is important since the fourth industrial revolution (Industry 4.0) depends more on highperformance computing and the co-existence of technology with human resources. A case study was conducted in a technology company in South Africa which had restructured their software department from a top-down reporting structure to a matrix structure (dual management system). This research identified gaps in the current roles of the managers and in the matrix structure itself. The focus of the research was to identify the factors that were negatively influenced by the matrix structure such as the quality of the projects produced, the level of risk involved and return on investment from the projects delivered. It was found that functional teams were hemorrhaging 9.56% of employees through resignations and projects were only achieving 94.17% of their objectives.
Description:
Digital Communication systems have matured to a point of acceptance in the last decade enormously based on IEC 61850 guidelines from Substation Automation System (SAS) perspective. However, these networks have issues related to latency, data clogging delays, errors etc. in the digital protection system. Measuring propagation delays and assessing the performance of IED's and other peripherals in an Ethernet topology play a critical role in relay coordination setting. This paper discusses issues associated with a digital network and addresses the problems that could mitigate spurious tripping or compromise the protection of the assets in the feeders leading to a reliable operation of the protection system. The discussion in this paper is carried out based on a case study related to a digital star topology using Optimized Network Engineering Tool (OPNET) software of a 132/22-kV zone substation. We report the practical case of measuring delay time during frame exchanges of Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV) messages amongst the IED' and peripherals.
Description:
Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
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Proceedings of the IEEE International Conference on Industrial Technology
Description:
Content-based image retrieval (CBIR) is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture) which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES) is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.
Description:
The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Similarity measure is an important research topic in image classification and retrieval. Given a type of image features, a good similarity measure should be able to retrieve similar images from the database while discard irrelevant images from the retrieval. Similarity measures in literature are typically distance based which measure the spatial distance between two feature vectors in high dimensional feature space. However, this type of similarity measures do not have any perceptual meaning and ignore the neighborhood influence in the similarity decision making process. In this paper, we propose a novel dissimilarity measure, which can measure both the distance and perceptual similarity of two image features in feature space. Results show the proposed similarity measure has a significant improvement over the traditional distance based similarity measure commonly used in literature.
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International Conference Image and Vision Computing New Zealand
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Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
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Studies in Health Technology and Informatics
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With the advancement of client-side on the fly web content generation techniques, it becomes easier for attackers to modify the content of a website dynamically and gain access to valuable information. A majority portion of online attacks is now done by WebInject. The end users are not always skilled enough to differentiate between injected content and actual contents of a webpage. Some of the existing solutions are designed for client side and all the users have to install it in their system, which is a challenging task. In addition, various platforms and tools are used by individuals, so different solutions needed to be designed. Existing server side solution often focuses on sanitizing and filtering the inputs. It will fail to detect obfuscated and hidden scripts. In this paper, we propose a server side solution using a machine learning approach to detect WebInject in banking websites. Unlike other techniques, our method collects features of a Document Object Model (DOM) and classifies it with the help of a pre-trained model.
Description:
With the emergence and extensive deployment of biometric based user authentication system, ensuring the security of biometric template is becoming a growing concern in research community. One approach of securing biometric data is cancellable biometric which transforms the original biometric features into a non-invertible form for enrolment and matching. However, most of the schemes for generating cancellable template are alignment-based requiring an accurate alignment of query and enrolled images, which is very difficult to achieve. In this paper, we propose an alignment-free technique for generating revocable fingerprint template that exploits the local features i.e., minutiae details in a fingerprint image. A rotation and translation invariant values are extracted from the neighbouring region of each minutia. The invariant values are then used as inputs in a transformation function and combined with a stored and a user-specific key based random vectors using the type and orientation information of the minutiae. Hence, by varying the stored and user-specific keys in the transformation, multiple application-specific templates can be generated to preserve users’ privacy. Besides, if the transformed template is compromised, a new template can be reissued by assigning different keys for transformation to achieve revocability. Furthermore, the proposed approach preserves the actual geometric relationships between the enrolled and query templates even after transformation and offers reasonable recognition rate. Experiments conducted on FVC2000 DB1 demonstrate that the proposed method exhibits promising performance in terms of recognition accuracy, computational complexity, security along with diversity, revocability and non-invertibility that are the key issues of cancellable template generation.
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ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy
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Due to increase in intrusion activities over internet, many intrusion detection systems are proposed to detect abnormal activities, but most of these detection systems suffer a common problem which is producing a high number of alerts and a huge number of false positives. As a result, normal activities could be classified as intrusion activities. This paper examines different data mining techniques that could minimize both the number of false negatives and false positives. C5 classifier’s effectiveness is examined and compared with other classifiers. Results should that false negatives are reduced and intrusion detection has been improved significantly. A consequence of minimizing the false positives has resulted in reduction in the amount of the false alerts as well. In this study, multiple classifiers have been compared with C5 decision tree classifier using NSL_KDD dataset and results have shown that C5 has achieved high accuracy and low false alarms as an intrusion detection system.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
Description:
The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
Description:
Kalman filter estimates the desired signal from the amount of measurement related to the extracted signal, which is widely used in engineering due to its simple calculation and easy programming on a computer. However, the basic theory originally proposed by Rudolf E. Kalman is for linear systems only, whereas a realistic physical system is often nonlinear. Extended Kalman Filter (EKF) solves nonlinear filtering problems. In this paper, we focus on issues related with targeted object being occluded We combine EKF and Meanshift to track the moving object. Once the object position is predicted by EKF in the center of the object, then the Meanshift algorithm iterates over the initial value of EKF estimation to track the object. Experiments show that the method reduces the object search time and improves the accuracy of the object tracking.