Internet of things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4. Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Tab. 14 at the end of Section IV. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature.

The paper investigates the determinants of foreign direct investment (FDI) in Vietnam in 2000-2019 period. This study uses difference Generalized Methods of Moments (GMM) and Pooled Mean Group (PMG) to analyse panel data officially provided by General Statistical Office of Vietnam. The results show that market size impacts positively significant on FDI attraction: 1% -1.45% (PMG) and 1% -1.25% (GMM). Besides, some other factors have positive influences as labor force, macroeconomic policy, macroeconomic stability and skilled labor. Meantime, the trade openness negatively affects FDI inflows in the short-term, while not being statistically significant in the long-term. Moreover, economic shocks often have a negative impact on FDI inflows. The findings of this study lead to the following recommendations. First, authorities should pay special attention to encourage economic growth rate in Vietnam to expand market size because this is the first priority of foreign investors. Second, authorities need to continue increasing the rate of skilled labor, especially highly qualified management force, engineers and well-skilled workers. Third, the authorities should adjust trade openness to boost the role of its determinant in attracting FDI inflows. Fourth, macroeconomic stability needs to be governed by international standards in order to secure the belief of foreign investors in the long-term.

The purpose of this paper is to investigate the impact of total factor productivity (TFP), institutional quality, and interactive variable between them on economic growth in 13 low-middle income countries in Asia for the period 2000-2018. The paper uses the difference Generalized Method of Moments (GMM) to explore the dataset provided by the World Bank. The empirical results show that TFP and the interactive variable positively impact on the economic growth, while the institutional determinants have a negative influence. The negative effect is explained by the weak institutions in these low-middle income countries. The findings of the study suggest two points. First, the government should continue to improve TFP, which is associated with the application of technical advances, technological innovations, improvement of management methods, and skilled workers. Second, far more important, is that the authorities should pay special attention to implement institutional reform and strengthen the governance in the future. The successful experiences from Japan, Korea and Singapore will help other governments in Asian low-middle income countries to build developmental state. Probably, the developmental state actively interfere in the market to promote and realize the development goals. By doing so, these economies might overcome the so-called "middle-income trap".

The paper examines the role of some determinants of economics, politics and institutions on the budget deficit volatility in some countries of the Association of South East Asian Nations (ASEAN) such as Indonesia, Thailand and Vietnam. The paper uses the fixed effects model (FEM) and the random effects model (REM) to investigate panel data of these countries in the period of 1990-2018. Moreover, the study also explores ordinary least square (OLS) to analyze time-series data for each country in the same period to make comparison among them. The economic data is collected from international financial statistics and world development indicators. The data on political variables are collected from International Country Risk Data Guide (ICRG). The empirical results both confirm that corruption and political stability are important indicators of budget deficit. Besides, the paper suggests authorities should pay more attention on improving the institutional setup of the economy in order to avoid high and unstable deficit. The findings offer new insight on the budget deficit in essence and suggest that the most important thing need to be done ahead is to strongly implement anti-corruption actions. By doing so, the status of budget deficit would be remarkably improved immediately.