Kuinam J. Kim

Recent trends on mobile computing and future networks

With the development of IT convergence technologies to improve quality of life, users can now more easily access useful information. A convergence system represents an environment that is able to provide personal services by configuring various devices and sensors based on both wire and wireless networks. Further, diverse and far-reaching information is being produced fast and distributed instantly in digitized format. Studies on mobile computing are continuously presenting more efficient ways of delivering information to more users. Mobile computing is a technology that provides a service automatically based on perceived situational information in personal and ubiquitous environments. Ubiquitous computing is characterized by users who are focused on a virtual space established by computers and networks. However, mobile computing groups of computers work through various sensors that exist in the real world. Users are able to receive various personal services using many different types of mobile computing resources within an internal/external space, without limitations in time or space [1, 2]. Previously, users had to convey their intentions using standard input devices and obtain the results on an output device. On the contrary, in a distributed and mobile computing environment, lifelog, sensors, big data, and computing resources are ubiquitous in a user’s everyday life. To provide personal services according to various lifestyles, these computing resources should be aware of a user’s intentions and the surrounding environment, as well as provide optimal services [3, 4].

Implementation of a cost-effective home lighting control system on embedded Linux with OpenWrt

In recent years, the significance of greenhouses has been increased greatly because the world has been facing serious problems with energy as its growing demand. At the same time, home automation systems have been steadily gaining popularity; growing toward smart home based on Cloud technology. This paper introduces a cost-effective home energy saving system based on a small embedded system with remote controlling feature. For this purpose, the system is composed of a wireless router based on embedded Linux for the platform to develop a low-cost energy control server and a smart phone for remote light control app. The prototype system was implemented by porting OpenWrt onto the wireless router which is connected with an interface board with LEDs attached. The remote access and GUI function were implemented by TCP/IP programming using Apple iPhone. The operation of the remote control system was verified by socket communication between the smart phone and the wireless router, and by USB communication between the wireless router and the interface board. The implementation result shows that an OpenWrt-based wireless router can give benefits of saving energy and safety through lighting control.

Categorization for grouping associative items using data mining in item-based collaborative filtering

Recommendation systems have been investigated and implemented in many ways. In particular, in the case of a collaborative filtering system, the most important issue is how to manipulate the personalized recommendation results for better user understandability and satisfaction. A collaborative filtering system predicts items of interest for users based on predictive relationships discovered between each item and others. This paper proposes a categorization for grouping associative items discovered by mining, for the purpose of improving the accuracy and performance of item-based collaborative filtering. It is possible that, if an associative item is required to be simultaneously associated with all other groups in which it occurs, the proposed method can collect associative items into relevant groups. In addition, the proposed method can result in improved predictive performance under circumstances of sparse data and cold-start initiation of collaborative filtering starting from a small number of items. In addition, this method can increase prediction accuracy and scalability because it removes the noise generated by ratings on items of dissimilar content or level of interest. The approach is empirically evaluated by comparison with k-means, average link, and robust, using the MovieLens dataset. The method was found to outperform existing methods significantly.

A Study on Malicious Codes Pattern Analysis Using Visualization

The expansion of internet technology has made convenience. On the one hand various malicious code is produced. The number of malicious codes occurrence has dramatically increasing, and new or variant malicious code circulation very serious, So it is time to require analysis about malicious code. The being so malicious code pattern extract for malicious code properties of anti-virus company. Visualization possible to make one image for thousands upon thousands of malicious code. and It is possible to extract unseen pattern. Therefore this paper of object is various malicious code analysis besides new or variant malicious code type or form deduction using visualization of strong. Thus this paper proposes unseen malicious code pattern extract.

A survey of deep learning-based network anomaly detection

A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.

A New Virtualized Environment for Application Deployment Based on Docker and AWS

The setup environment and deployment of distributed applications is a human intensive and highly complex process that poses significant challenges. Nowadays many applications are developed in the cloud and existing applications are migrated to the cloud because of the promising advantages of cloud computing. The very core of cloud computing is virtualization. In this paper, we will look at application deployment with Docker. Docker is a lightweight containerization technology that has gained widespread popularity in recent years. It uses a host of the Linux kernel’s features such as namespaces and croup’s to sandbox processes into configurable virtual environments. Presenting two common serious challenging scenarios in the software development environment, we propose a multi-task PaaS cloud infrastructure using Docker and AWS services for application isolation/optimization and rapid deployment of distributed applications.

A feature selection approach to find optimal feature subsets for the network intrusion detection system

The performance of network intrusion detection systems based on machine learning techniques in terms of accuracy and efficiency largely depends on the selected features. However, choosing the optimal subset of features from a number of commonly used features to detect network intrusion requires extensive computing resources. The number of possible feature subsets from given n features is 2

Flexible authorization in home network environments

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