The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.

The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.

In healthcare research, the reliability of input data is essential. However, missing data is a common incident in this field for various reasons. Current research mainly focuses on developing new data imputation methodologies, while there is a need for studying on a global evaluation of existing algorithms. In this research, we compared the performance of four influential missing data imputation algorithms, Regularized Expectation-Maximization (EM), Multiple Imputation (MI), kNN Imputation (kNNI) and Mean Imputation on two real health care datasets: (1) MHEALTH dataset and (2) the University of Queensland Vital Signs dataset. Under the Missing Completely At Random (MCAR) assumption, Root Mean Squared Error (RMSE) and execution time were used as best performing evaluation criteria. The experimental analysis suggests that EM is the imputation algorithm which is expected to be a good choice to deal with the problem of missing data in the healthcare area.

This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

The smart grid is a complicated system consisting of communication network and power grid components. There are various powerful simulation tools for communication networks, as well as power systems. However, co-simulation tools are required to reproduce the interaction between cyber-physical components. We conducted a survey overview of various cosimulation tools and their characteristics applicable to smart grid research. We determined that the combination of FCNS, GridLAB-D and ns-3 is a promising direction for smart grid study, improving co-simulation speed by 20%. By applying these tools and the IEEE 13 Node Test Feeder Model, we conducted a case study on the impact of security threats on smart grid demand/response and dynamic pricing applications. The impact of fake data injection and jamming attacks are obvious as a result of our simulation. The findings support related research in the field and can be used for cybersecurity training.