Audit education in a socialist oriented market economy – the case of Vietnam
- Dang, Ky
- Authors: Dang, Ky
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The objective of the research project is to contribute to the understanding of the auditing profession in Vietnam. In particular, it provides information on the challenges facing auditors in an emerging economy where economic transformation is in progress and where auditing, as a profession, is under development. Auditors operate in an environment of conflicting priorities where they must maintain independence and objectivity in discharging their responsibilities to stakeholders and society. In Vietnam, the audit profession only came into existence in 1986 when the country embarked on its new economic model. Whilst studies have been conducted on the status of current accounting practice in Vietnam, studies regarding the audit profession have been limited. In this research project an examination of the issues affecting audit quality in Vietnam are investigated and suggestions for changes to address the deficiencies are made. In particular, the project focuses on the relevance and appropriateness of the education of auditors. A national survey of accountants, auditors and accounting academics in Vietnam was undertaken. The survey results indicate that in Vietnam there are deficiencies in audit practices over and above those commonly observed in other countries. These deficiencies are the result of the unique history of Vietnam, the current stage of economic development and the education system for auditors. From an auditing perspective, the slow adaptation of the education system to the new economic environment is having negative effects on accounting graduates and their employment prospects. Although inadequate training was identified as the single most important factor affecting audit quality, the ineffective enforcement regime was also a contributing factor. This research project indicates that there is a need for an overhaul of the current education system in Vietnam and for universities to develop an accounting and auditing curriculum that meets the needs of employers while complying with government’s education objectives and international standards of auditing and accounting.
- Description: Doctor of Philosophy
- Authors: Dang, Ky
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The objective of the research project is to contribute to the understanding of the auditing profession in Vietnam. In particular, it provides information on the challenges facing auditors in an emerging economy where economic transformation is in progress and where auditing, as a profession, is under development. Auditors operate in an environment of conflicting priorities where they must maintain independence and objectivity in discharging their responsibilities to stakeholders and society. In Vietnam, the audit profession only came into existence in 1986 when the country embarked on its new economic model. Whilst studies have been conducted on the status of current accounting practice in Vietnam, studies regarding the audit profession have been limited. In this research project an examination of the issues affecting audit quality in Vietnam are investigated and suggestions for changes to address the deficiencies are made. In particular, the project focuses on the relevance and appropriateness of the education of auditors. A national survey of accountants, auditors and accounting academics in Vietnam was undertaken. The survey results indicate that in Vietnam there are deficiencies in audit practices over and above those commonly observed in other countries. These deficiencies are the result of the unique history of Vietnam, the current stage of economic development and the education system for auditors. From an auditing perspective, the slow adaptation of the education system to the new economic environment is having negative effects on accounting graduates and their employment prospects. Although inadequate training was identified as the single most important factor affecting audit quality, the ineffective enforcement regime was also a contributing factor. This research project indicates that there is a need for an overhaul of the current education system in Vietnam and for universities to develop an accounting and auditing curriculum that meets the needs of employers while complying with government’s education objectives and international standards of auditing and accounting.
- Description: Doctor of Philosophy
Robust Mobile Malware Detection
- Authors: Khoda, Mahbub
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The increasing popularity and use of smartphones and hand-held devices have made them the most popular target for malware attackers. Researchers have proposed machine learning-based models to automatically detect malware attacks on these devices. Since these models learn application behaviors solely from the extracted features, choosing an appropriate and meaningful feature set is one of the most crucial steps for designing an effective mobile malware detection system. There are four categories of features for mobile applications. Previous works have taken arbitrary combinations of these categories to design models, resulting in sub-optimal performance. This thesis systematically investigates the individual impact of these feature categories on mobile malware detection systems. Feature categories that complement each other are investigated and categories that add redundancy to the feature space (thereby degrading the performance) are analyzed. In the process, the combination of feature categories that provides the best detection results is identified. Ensuring reliability and robustness of the above-mentioned malware detection systems is of utmost importance as newer techniques to break down such systems continue to surface. Adversarial attack is one such evasive attack that can bypass a detection system by carefully morphing a malicious sample even though the sample was originally correctly identified by the same system. Self-crafted adversarial samples can be used to retrain a model to defend against such attacks. However, randomly using too many such samples, as is currently done in the literature, can further degrade detection performance. This work proposed two intelligent approaches to retrain a classifier through the intelligent selection of adversarial samples. The first approach adopts a distance-based scheme where the samples are chosen based on their distance from malware and benign cluster centers while the second selects the samples based on a probability measure derived from a kernel-based learning method. The second method achieved a 6% improvement in terms of accuracy. To ensure practical deployment of malware detection systems, it is necessary to keep the real-world data characteristics in mind. For example, the benign applications deployed in the market greatly outnumber malware applications. However, most studies have assumed a balanced data distribution. Also, techniques to handle imbalanced data in other domains cannot be applied directly to mobile malware detection since they generate synthetic samples with broken functionality, making them invalid. In this regard, this thesis introduces a novel synthetic over-sampling technique that ensures valid sample generation. This technique is subsequently combined with a dynamic cost function in the learning scheme that automatically adjusts minority class weight during model training which counters the bias towards the majority class and stabilizes the model. This hybrid method provided a 9% improvement in terms of F1-score. Aiming to design a robust malware detection system, this thesis extensively studies machine learning-based mobile malware detection in terms of best feature category combination, resilience against evasive attacks, and practical deployment of detection models. Given the increasing technological advancements in mobile and hand-held devices, this study will be very useful for designing robust cybersecurity systems to ensure safe usage of these devices.
- Description: Doctor of Philosophy
- Authors: Khoda, Mahbub
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The increasing popularity and use of smartphones and hand-held devices have made them the most popular target for malware attackers. Researchers have proposed machine learning-based models to automatically detect malware attacks on these devices. Since these models learn application behaviors solely from the extracted features, choosing an appropriate and meaningful feature set is one of the most crucial steps for designing an effective mobile malware detection system. There are four categories of features for mobile applications. Previous works have taken arbitrary combinations of these categories to design models, resulting in sub-optimal performance. This thesis systematically investigates the individual impact of these feature categories on mobile malware detection systems. Feature categories that complement each other are investigated and categories that add redundancy to the feature space (thereby degrading the performance) are analyzed. In the process, the combination of feature categories that provides the best detection results is identified. Ensuring reliability and robustness of the above-mentioned malware detection systems is of utmost importance as newer techniques to break down such systems continue to surface. Adversarial attack is one such evasive attack that can bypass a detection system by carefully morphing a malicious sample even though the sample was originally correctly identified by the same system. Self-crafted adversarial samples can be used to retrain a model to defend against such attacks. However, randomly using too many such samples, as is currently done in the literature, can further degrade detection performance. This work proposed two intelligent approaches to retrain a classifier through the intelligent selection of adversarial samples. The first approach adopts a distance-based scheme where the samples are chosen based on their distance from malware and benign cluster centers while the second selects the samples based on a probability measure derived from a kernel-based learning method. The second method achieved a 6% improvement in terms of accuracy. To ensure practical deployment of malware detection systems, it is necessary to keep the real-world data characteristics in mind. For example, the benign applications deployed in the market greatly outnumber malware applications. However, most studies have assumed a balanced data distribution. Also, techniques to handle imbalanced data in other domains cannot be applied directly to mobile malware detection since they generate synthetic samples with broken functionality, making them invalid. In this regard, this thesis introduces a novel synthetic over-sampling technique that ensures valid sample generation. This technique is subsequently combined with a dynamic cost function in the learning scheme that automatically adjusts minority class weight during model training which counters the bias towards the majority class and stabilizes the model. This hybrid method provided a 9% improvement in terms of F1-score. Aiming to design a robust malware detection system, this thesis extensively studies machine learning-based mobile malware detection in terms of best feature category combination, resilience against evasive attacks, and practical deployment of detection models. Given the increasing technological advancements in mobile and hand-held devices, this study will be very useful for designing robust cybersecurity systems to ensure safe usage of these devices.
- Description: Doctor of Philosophy
The impacts of climate change on trade and foreign direct investment flows
- Authors: Barua, Suborna
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: A growing body of climate economics research suggests that climate change affects production, prices, distribution structures, investments and national income. Studies further describe international trade and climate related investments as key activities in climate impact mitigation and adaptation. However, despite its increasing relevance, the empirical link between climate change and international trade and investment remains largely unexplored. This thesis investigates the climate change impacts on trade and foreign direct investment (FDI) flows using static and dynamic panel estimations covering 102 countries. The modelling uses temperature and precipitation variability to separately evaluate changes in international trade from 1962 to 2014, and in FDI inflows from 1995 to 2014. The trade impacts estimations consider exports of total merchandise, agriculture and six agricultural sectors; while controlling for income, comparative advantage, productivity, domestic and trade policies, and climate zones. The FDI impacts modelling evaluates total and sectoral inflows, while controlling for income, market size, infrastructure, openness, financial development, the global financial crisis and climate zones. Results show that climate change significantly affects both exports and FDI inflows. In particular, temperature affects merchandise exports, negatively at the global and developing country level, and positively in high-income countries. Agricultural exports are negatively affected by temperature. At the sectoral level, oil-seeds and dairy are mostly affected. Precipitation effects are limited and mostly negative for agriculture. The FDI world aggregate flows respond mostly positively to both temperature and precipitation, and static estimations indicate a FDI positive response in developing countries. Furthermore, FDI sectoral estimations indicate a differentiated response. Findings could inform the formulation of trade and investment policies, at the national and global level, in consideration to the differential impacts of climate change across sectors, regions and economic status. Furthermore, these estimates could be used in projections considering climate change as a determinant of trade and investment flows.
- Description: Doctor of Philosophy
- Authors: Barua, Suborna
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: A growing body of climate economics research suggests that climate change affects production, prices, distribution structures, investments and national income. Studies further describe international trade and climate related investments as key activities in climate impact mitigation and adaptation. However, despite its increasing relevance, the empirical link between climate change and international trade and investment remains largely unexplored. This thesis investigates the climate change impacts on trade and foreign direct investment (FDI) flows using static and dynamic panel estimations covering 102 countries. The modelling uses temperature and precipitation variability to separately evaluate changes in international trade from 1962 to 2014, and in FDI inflows from 1995 to 2014. The trade impacts estimations consider exports of total merchandise, agriculture and six agricultural sectors; while controlling for income, comparative advantage, productivity, domestic and trade policies, and climate zones. The FDI impacts modelling evaluates total and sectoral inflows, while controlling for income, market size, infrastructure, openness, financial development, the global financial crisis and climate zones. Results show that climate change significantly affects both exports and FDI inflows. In particular, temperature affects merchandise exports, negatively at the global and developing country level, and positively in high-income countries. Agricultural exports are negatively affected by temperature. At the sectoral level, oil-seeds and dairy are mostly affected. Precipitation effects are limited and mostly negative for agriculture. The FDI world aggregate flows respond mostly positively to both temperature and precipitation, and static estimations indicate a FDI positive response in developing countries. Furthermore, FDI sectoral estimations indicate a differentiated response. Findings could inform the formulation of trade and investment policies, at the national and global level, in consideration to the differential impacts of climate change across sectors, regions and economic status. Furthermore, these estimates could be used in projections considering climate change as a determinant of trade and investment flows.
- Description: Doctor of Philosophy
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