Computing and Information Systems - Theses

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    Safe acceptance of zero-confirmation transactions in Bitcoin
    Yang, Renlord ( 2016)
    Acceptance of zero confirmation transactions in Bitcoin is inherently unsafe due to the lack of consistency in states between nodes in the network. As a consequence of this, Bitcoin users must endure a mean wait time of 10 minutes to accept confirmed transactions. Even so, due to the possibility of forks in the Blockchain, users who may want to avoid invalidation risks completely may have to wait up to 6 confirmations, which in turn results in a 60 minute mean wait time. This is untenable and remains a deterrent to the utility of Bitcoin as a payment method for merchants. Our work seeks to address this problem by introducing a novel insurance scheme to guarantee a deterministic outcome for transaction recipients. The proposed insurance scheme utilizes standard Bitcoin scripts and transactions to produce inter-dependent transactions which will be triggered or invalidated based on the occurance of potential doublespend attacks. A library to setup the insurance scheme and a test suite was implemented for anyone who may be interested in using this scheme to setup a fully anonymous and trustless insurance scheme. Based on our test in Testnet, our insurance scheme was successful at defending against 10 out of 10 doublespend attacks.
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    Automatic caloric expenditure estimation with smartphone's built-in sensors
    Cabello Wilson, Nestor Stiven ( 2016)
    Fitness-tracking systems are technologies commonly used to enhance peoples' lifestyles. Feedback, usability, and ease of acquisition are fundamental to achieving the good physical condition goal. Users need constant motivation as a way to keep their interest in the fitness system and consequently, continue on a healthy lifestyle track. However, although feedback is increasingly being incorporated in many fitness-tracking systems, usability and ease of acquisition are remaining shortcomings that need to be enhanced. Features such as automatic activity identification, low-energy consumption, simplicity and goals-achieved notifications provide a good user experience. Nevertheless, most of these functions require the acquisition of a relatively expensive fitness-tracking device. Smartphones provide a partial solution by allowing users an easy access to multiple fitness applications, which reduce the need for purchasing another gadget. Nonetheless, improvements in the user experience are still necessary. In the other hand, wearables devices satisfy the usability, however, the cost of their acquisition represents an impediment to some users. The system proposed in this research aims to handle these issues and offers a solution by combining the benefits from mobile applications such as feedback and ease of acquisition, with the usability that wearable devices provide, into a smartphone Android application. Data collected from a single user while performing a series of common daily activities namely walking, jogging, cycling, climbing stairs, and walking downstairs, was used to classify and provide an automatic identification of these activities with an overall accuracy of 91%, and identifying the stairs activities with an accuracy of 81%. Finally, the caloric expenditure, which we considered the most important metric for motivating a user to perform a physical activity, was estimated by following the oxygen consumption equations from the American College of Sports Medicine (ACSM).
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    Exploring knowledge leakage risk in knowledge-intensive organisations: behavioural aspects and key controls
    Altukruni, Hibah Ahmed ( 2019)
    Knowledge leakage poses a critical risk to the competitiveness advantages of knowledge-intensive organisations. Although knowledge leakage is a human-centric security issue, little is known in relation to the key factors of individual-level leaking behaviour. Therefore, the aim of this thesis was to explore security practitioners’ perspectives on the key enablers and inhibitors of behavioural knowledge leakage risk in the context of knowledge-intensive organisations. An exploratory, qualitative design was used to carry out the study. Moreover, seven security practitioners working in Australian organisations were recruited to participate in this research. The data were collected using semi-structured questions via two focus group discussions. The discussion sessions lasted between 90 and 120 minutes, including a 10-minute break. The sessions were audio recorded, transcribed, and thematically analysed following Braun and Clarke’s (2006) strategy. Furthermore, two main trends emerged from the analysed data. First, ‘interpersonal enabling factors’ included leaking behaviours and employees’ personality’ traits. Second, contributing ‘organisational practices around knowledge leakage mitigation’ included poor knowledge sensitivity classification systems and poor knowledge security management practices. In conclusion, it is essential that security practitioners address the key identified factors of behavioural leakage risk to mitigate the leaking incidents effectively. Three key security practices that were found to have a superior impact in mitigating leaking enablers included human resource management practices, knowledge security training and awareness practices, and compartmentalisation.
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    Protecting organizational knowledge: a strategic perspective framework
    DEDECHE, AHMED ( 2014)
    Organizational knowledge is considered a valuable resource for providing competitive advantage. Extensive research has been done on strategies to encourage knowledge creation and sharing. However, limited research has been done on strategies for protecting this valuable resource from the risk of leakage. This research aims to contribute in bridging this gap by two contributions: developing a model that describes knowledge leakage, and providing a framework of strategies for protecting competitive organisational knowledge. The research is grounded on two bodies of literature: Knowledge management and information security. The research aims for identifying security strategies in literature and adapting them to address knowledge protection needs.
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    The dark web as a phenomenon: a review and research agenda
    Gupta, Abhineet ( 2018)
    The internet can broadly be divided into three parts: surface, deep and dark among which the latter offers anonymity to its users and hosts. The dark web has become notorious in the media for being a hidden part of the web where all manner of illegal activities take place. The more restrictions placed upon the free exchange of information, goods and services between people the more likely there exist hidden spaces for it to take place. The ‘black market’ of the internet – the dark web - represents such a hidden space. This review looks at the purposes it is widely used for with an emphasis on cybercrime, and how the law enforcement plays the role of its adversary. The review describes these hidden spaces, sheds light on their history, the activities that they harbour – including cybercrime, the nature of attention they receive, and methodologies employed by law enforcement in an attempt to defeat their purpose. More importantly, it is argued that these spaces should be considered a phenomenon and not an isolated occurrence to be taken as merely a natural consequence of technology. The review is conducted by looking at existing literature in academic journal databases. It contributes to the area of the dark web by serving as a reference document and by proposing a research agenda.
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    A secure innovation process for start-ups: Minimising knowledge leakage and protecting IP
    Pitruzzello, Sam ( 2016)
    Failing to profit from innovations as a result of knowledge leakage is a key business risk for high-tech start-ups. Innovation is central to the success of a start-up and their competitive advantage in the market place therefore methods to protect intellectual property (IP) and minimise knowledge leakage is crucial. However, high-tech start-ups have limited resources rendering them more vulnerable to knowledge leakage risks compared to mature enterprises. Unfortunately, research on knowledge leakage and innovation processes falls short of addressing the needs of high-tech start-ups. Since knowledge leakage can occur in a number of ways involving many scenarios, organisations typically employ a variety of IP protection and knowledge leakage mitigation methods to minimise the risks. This minor thesis fills the research gaps on innovation processes and knowledge leakage for start-ups. A literature review was conducted into the bodies of research on knowledge leakage and innovation. Following the literature review, a secure innovation process (SIP) model was developed from the research. SIP includes the concept of the risk window which allows a start-up to identify, assess and manage knowledge leakage risks at various stages in the innovation process.
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    An exploratory study of information security auditing
    Kudallur Ramanathan, Ritu lakshmi ( 2016)
    Management of Information security in organizations is a form of risk management where threats to information assets are managed by implementing various controls. An important task in this cycle of Information Security risk management is Audit, whose function is to provide assurance to organizations that their security controls are indeed working as intended. Numerous frameworks and guidelines are available for auditing Information security. However, there is scant empirical evidence for the process followed in practice. This research explores how security audits are conducted in practice. In order to do so, a qualitative study is conducted where 11 auditors are interviewed. The findings indicate a gap between what is expected of audit and what actually happens in practice. On exploring the Accounting roots of audit, we postulate that this gap is due to the differences in conceptualization of risk between the Accounting and Information Security discipline.
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    Improving performance of multi-agent cooperation using epistemic planning
    Alshehri, Abeer Dhafer G. ( 2017)
    In multi-agent systems, a communication process is essential among agents to interact and coordinate their actions, and thus achieve their goal. However, communication has a related cost which affects the overall system performance. In this thesis, we draw inspiration from studies of the epistemic planning framework to develop a communication model for agents that allows them to cooperate and make communication decisions effectively within a multi-agent planning task. Our approach aims to develop a compact model that involves a communication action to be a part of planning task as a natural action. In simulated tasks motivated by a disaster scenario, we aim to investigate whether agents can cooperate effectively and achieve higher performance using a proposed communication model (a selective model).
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    Unsupervised all-words sense distribution learning
    Bennett, Andrew ( 2016)
    There has recently been significant interest in unsupervised methods for learning word sense distributions, or most frequent sense information, in particular for applications where sense distinctions are needed. In addition to their direct application to word sense disambiguation (WSD), particularly where domain adaptation is required, these methods have successfully been applied to diverse problems such as novel sense detection or lexical simplification. Furthermore, they could be used to supplement or replace existing sources of sense frequencies, such as SemCor, which have many significant flaws. However, a major gap in the past work on sense distribution learning is that it has never been optimised for large-scale application to the entire vocabularies of a languages, as would be required to replace sense frequency resources such as SemCor. In this thesis, we develop an unsupervised method for all-words sense distribution learning, which is suitable for language-wide application. We first optimise and extend HDP-WSI, an existing state-of-the-art sense distribution learning method based on HDP topic modelling. This is mostly achieved by replacing HDP with the more efficient HCA topic modelling algorithm in order to create HCA-WSI, which is over an order of magnitude faster than HDP-WSI and more robust. We then apply HCA-WSI across the vocabularies of several languages to create LexSemTm, which is a multilingual sense frequency resource of unprecedented size. Of note, LexSemTm contains sense frequencies for approximately 88% of polysemous lemmas in Princeton WordNet, compared to only 39% for SemCor, and the quality of data in each is shown to be roughly equivalent. Finally, we extend our sense distribution learning methodology to multiword expressions (MWEs), which to the best of our knowledge is a novel task (as is applying any kind of general-purpose WSD methods to MWEs). We demonstrate that sense distribution learning for MWEs is comparable to that for simplex lemmas in all important respects, and we expand LexSemTm with MWE sense frequency data.
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    Large scale real-time traffic flow prediction using SCATS volume data
    Panda, Rabindra ( 2016)
    Road traffic congestion is a global issue that results in significant wastage of time and resources. Rising population, urbanisation, growing economies and affordable personal vehicles aggravate the issue. Many urban cities have been trying to mitigate this by expanding and modernising the transportation infrastructure. Even though increasing the road capacity accommodates the travel demands, studies have shown this does not eliminate the congestion problem. Hence, since 1970’s advanced traffic management systems have been used to address the issue of congestion. But for these systems to increase their operational efficiencies and fully realise their effectiveness, they need to have the predictive capabilities in the short term, usually ranging between few seconds to few hours. The research in short term traffic prediction has been active since the 1970’s. Numerous models have been proposed to use the traffic data collected by in- ductive loop detectors for short term traffic prediction. Most of the works have shown promising results through experiments at particular locations, however we are still to find a robust and globally adaptable solution. In last decade the attention have shifted from theoretically well established parametric methods to non parametric data driven algorithms. This work is an extension to that. Neural networks have always been one of the most capable mathematical models that can model complex non-linear relations. Up to 2006, their use have been hindered by practical issues related to the training. But recent breakthroughs in new ways of training deep neural architectures have made them reemerged as victors by realising the capabilities they had promised. In this thesis we study and extend their applications to short term traffic predictions. We applied three deep recurrent neural networks (Simple RNN, LSTM and GRU) in predicting the short term traffic volumes. The goal was to use both the temporal and spatial relationships that are present in the traffic flow data. We used these networks at univariate and multivariate settings to make predictions at single location and multiple locations respectively. For this work we used the volume data collected by VicRoads in Melbourne. We compared the results of our work with several existing methods and found promising results.