Ying-Feng Hsu 교수

Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In this paper, we propose a deep reinforcement learning-based (DRL) for anomaly network intrusion detection system. Our system has the ability of self-updating to reflect new types of network traffic behavior. This study includes three major contributions. First, to show the overall applicability of our approach, we demonstrate our work through two

With information technology growing and rapidly increasing, ubiquitous networking technology generates a massive amount of data and is integrated into our daily life. Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In general, there are two types of NIDS: signature-based (SNIDS) and anomaly-based (ANDIS). Most modern NIDS solutions are signature-based techniques, which require a routine signature

With information technology growing and rapidly increasing ICT equipment, a massive amount of data have been generated and stored in the cloud. However, the majority of them are infrequently accessed data. Data temperature describes the frequency of data access. Hot storage is dedicated to storing frequently accessed data while cold storage is designed for infrequently accessed data. To cope with the issue of exponential data growth in cloud, it is essential to allocate different categories of d

The number and scale of data centers are rapidly increasing, due to the growing demand for cloud computing services. Cloud computing infrastructure relies on a massive amount of information and communication technology (ICT) equipment, which consume an enormous amount of power. Power saving and energy optimization have therefore become essential goals for data centers. An enhanced data center energy management system (DEMS) provides a solution for data center power consumption based on its coord

The number and scale of data centers are both rapidly increasing due to a continuously growing demand for cloud computing services from many areas. Cloud computing infrastructure relies on a massive amount of HPC servers to process millions of tasks and consumes an enormous amount of power. The implementation of advanced task allocation technology provides a solution for energy efficiency and has therefore become an essential goal for data centers. In this paper, we propose a novel CPU-intensive

The exponential growth of computer networks and network applications has also increased the incidence of cyberattacks. Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communications and information. Many machine learning approaches have been proposed for DoS/DDoS detection and mitigation. However, most of these are based on using synthetic benchmark datasets, which may not thoroughly reflect real network DDoS attack patterns a

Metagenomic shotgun sequencing enables us to explore diverse DNA sequences from viruses, bacteria, and eukaryotic microbes in complex samples. As the continuous advancement of sequencing technology generates a massive amount of sequencing data, its overall computational complexity has become a major challenge for traditional database sequence comparison methods. Studies have shown that deep learningoriented methods have been widely adopted to solve many classification problems, including those i

In this paper, we present an early warning system for patients in the emergency department. Our proposed system includes data processing steps that transform raw clinical data streams into useful information that facilitates clinical decision making for the early warning. We tested the proposed approach in a medical monitoring system, which takes physiologic data and predicts in which clinical setting the data is most likely to be seen. To demonstrate the high utility of our approach, we conduct

The paper presents a new approach to reducing power consumption in data centers by optimizing server workload allocation and considering the impact on cooling impact from air conditioning (HVAC) systems, such as server static pressure difference and ambient temperature. We built highly accurate generic server power prediction models by investing in more than 20 algorithms. We proposed a workload allocation optimization (WAO) algorithm that extends Kubernetes API and uses it to evaluate energy ef

This dissertation presents a data processing architecture for efficient data warehousing from historical data sources. The present work has three primary contributions. The first contribution is the development of a generalized process data warehousing (PDW) architecture that includes multilayer data processing steps to transform raw data streams into useful information that facilitates data-driven decision making. The second contribution is exploring the applicability of the proposed architectu

We consider a novel query processing approach for data analysis in Process Data Warehouse system that performs summarization and discovery of trends in dynamic data from complex processes. In particular, we introduce an algebraic framework that can be used as a basis for algebraic optimization in PDW system.

With the continual growth of low-cost and high-throughput DNA sequence technology, the scale and amount of next-generation sequencing (NGS) datasets are continually increasing in many genomics research areas. Shotgun metagenomics sequencing provides comprehensive information on microorganisms, based on complex samples of the ecosystem. Due to challenges of its scale and computational complexity, efficient sequence processing and analyzing tools are needed. In this paper, we propose a novel high-

This paper presents an energy-efficient operational strategy for data centers by optimizing server workload allocation and air conditioning settings based on power consumption models for servers and air conditioners. These models were developed using control parameters accessible to operators of both systems and included static pressure difference as an explanatory variable. This approach allowed the models to be used generically (not sitespecific), ensuring their applicability across various in