Vol. 45, No. 4,
Apr. 2018
Digital Library
Design and Implementation of Security System for Providing Secure Boot and Firmware Update in Low-end IoT Device
Kiyeong Lee, Byoungseon Kim, Jinsung Cho
http://doi.org/10.5626/JOK.2018.45.4.321
Low-end IoT devices are problematic due to the many limitations involved in applying IoT devices to various existing security solutions. This is because most security solutions are targeted at high-performance PC environments. These limitations are causing steadily increasing technical security vulnerabilities and various security threats to IoT devices. In this paper, we propose a secure boot and firmware update system that can be applied in a constrained environment. At the secure boot, the proposed system verifies the integrity of the firmware of the device. The secure firmware update performs reliability verification of the subject attempting to update. Finally, we analyze the security performance of the proposed system by simulating various threats that may occur in low-end IoT devices.
VirtIO-trace: An Unified Tool for Analyzing I/O Characteristics on NVMe SSD in Virtualized Environments
http://doi.org/10.5626/JOK.2018.45.4.332
Virtualization technology plays a significant-role in diverse computing environments such as cloud computing and data center. CPU manufacturers actively provide hardware-based virtualization techniques for virtualization systems. NIC manufacturers also support virtualization techniques to improve network I/O performance. However, there is a significant performance degradation in storage I/O virtualization, and many studies attempted to overcome this problem. Recently, NVMe(Non-Volatile Memory Express) SSDs(Solid State Drives) have become increasingly popular as storage devices for high-performance virtualized I/O systems. However, such fast storage devices cannot improve I/O performance significantly against one’s expectation. To optimize the storage I/O optimization performance, we need an I/O tracking and analysis tool. In this paper, we propose a novel tool that can monitor I/O behaviors on NVMe SSD in virtualized environments. The tool, which we refer to as VirtIO-trace, basically allows to trace I/O requests and their timing information like the existing blktrace. However, it differs from the traditional tools in that it provides NVMe SSD specific information such as queue status and submission/completion statistics, and virtualization specific information such as I/O processing time in VM/host systems. We implemented the tool in the KVM virtualization system. Experimental results show that the tool can collect I/O information in real time, which can be usefully exploited in analyzing I/O characteristics and exploring a new policy for enhancing performance and fairness on management of NVMe SSD in virtualization systems.
A Comparative Study of C Program Mutation Tools for Effective Mutation Analysis: A Case Study of Proteum and Milu
Yunho Kim, Hyunwoo Kim, Woong-gyu Yang, Moonzoo Kim
http://doi.org/10.5626/JOK.2018.45.4.342
Mutation analysis generates mutants of a target program by applying syntactic changes to the source code and analyzes the difference of execution results of the mutants from those of the original program. For effective mutation analysis, mutant generation tools should be able to generate effective program mutants. For example, a mutant that is semantically equivalent to the original program or another mutant is not an effective mutant, because it does not generate an execution result different from that of the original program or another existing mutant. This paper presents a comparative study of two mutant generation tools for C programs, Proteum and Milu. To generate effective mutants effectively, we generated a canonical form of mutated expressions and removed duplicated mutants that have the same canonical form as that of other mutants. We applied Proteum and Milu to four Linux/Unix utilities in the SIR benchmark and showed that 48.7% and 46.4% of mutants generated by Proteum and Milu were effective mutants on average, respectively.
Methods for Analyzing Preference Tendencies and Activity Patterns with Audio Contexts
Hyun Jung La, Moon Kwon Kim, Han Ter Jung, Soo Dong Kim
http://doi.org/10.5626/JOK.2018.45.4.348
Recently, there has been a trend for collecting various rich personal contexts, such as social network services (SNS), user created contents (UCC), and digital diaries. Because of this trend, much more attention to semantic analysis with personal contexts is being paid. The semantic analysis allows users to analyze and understand diverse aspects of their lives such as their lifestyle and quality of life which are not easily recognized by them. Hence, in this paper, we propose a process to infer semantics from personal contexts, more specifically, audio contexts. This paper is focused on proposing detailed algorithms for analyzing user’s preference tendencies and activity patterns. To evaluate the proposed methods, we apply them to developing a system, called Smart Diary System, which is used to analyze a user’s preference tendency and activity pattern from audio diaries, and we present experiment results with the system. We expect to use the proposed process and algorithms in various application domains such as personal secretary service, recommendation services, and advertising services.
Approach for Learning Intention Prediction Model based on Recurrent Neural Network
Sung-hyuk Bang, Seok-Hyun Bae, Hyun-Kyu Park, Myung-Joong Jeon, Je-Min Kim, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.4.360
Several studies have been conducted on human intention prediction with the help of machine learning models. However, these studies have indicated a fundamental shortcoming of machine learning models since they are unable to reflect a long span of past information. To overcome this limitation, this paper proposes a human intention prediction model based on a recurrent neural network(RNN). For performing predictions, the RNN model classifies the patterns of time-series data by reflecting previous sequence patterns of the time-series data. For performing intention prediction using the proposed model, an RNN model was trained to classify predefined intentions by using attributes such as time, location, activity and detected objects in a house. Each RNN node is composed of a long short-term memory cell to solve the long term dependency problem. To evaluate the proposed intention prediction model, a data generator based on the weighted-graph structure has been developed for generating data on a daily basis. By incorporating 23,000 data instances for training and testing the proposed intention prediction model, a prediction accuracy value of 90.52% was achieved.
A Feature Selection Technique in the Neural Network for Demand Forecasting of Mobile Payment System
Ho-Joon Kim, Yun-Seok Cho, Kyungmi Kim
http://doi.org/10.5626/JOK.2018.45.4.370
In this paper, we present a time series prediction technique based on neural network as a methodology for forecasting service demand of mobile payment system. We propose a two-stage neural network model for the feature selection process and the prediction process. Three types of fuzzy membership functions were adopted for the representation of feature data, and a hyperbox-based neural network model is used for the evaluation of feature relevance factor. The proposed feature selection technique reduces the amount of computation and eliminates erroneous feature data in the learning data set. We evaluated the usefulness of the proposed method through experiments using two years of data obtained form actual smart campus systems.
A Comparative Study of Machine Learning Algorithms for Diagnosis of Ischemic Heart Disease
Pyoung-Woo Park, Min-Koo Kim, Hong-Seok Lim, Duk-Yong Yoon, Seok-Won Lee
http://doi.org/10.5626/JOK.2018.45.4.376
In recent years, studies on artificial intelligence have been actively conducted, and artificial intelligence technology supports accurate and efficient decision-making for mankind. Also, the accumulation of medical knowledge and related data is accelerating, and studies on diagnosis of diseases through artificial intelligence technology are being carried out briskly. In this study, I chose a representative cardiovascular disease, specifically ischemic heart disease, as a research domain, and analyzed the available algorithms comparing effective approaches in the medical expert system for diagnosis of the disease. Concretely, the purpose of the study is to assist medical experts and physicians based on the initial patient record data, help them to explain the cause of ischemic heart disease, and minimize unnecessary related tests. In addition, the experimental data can be configured so that medical professionals can use them as learning models, thereby maximizing their experience and knowledge efficiently.
Opinion Classification in Professional Sports Fan Sites using Topic Keyword-Based Sentiment Analysis
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
http://doi.org/10.5626/JOK.2018.45.4.390
In this study, we propose the classification method using topic keyword-based sentiment analysis through the posts of professional sports fan sites in Korea. We studied ways to take into account the use of special communication methods or vocabulary in the community and defined keywords based on the characteristics of the topic or frequency of the community"s words. In addition, we presented a new sentiment analysis approach that utilizes the use of keyword pools and the proximity relation to keywords. Through three years of actual community dataset, sentiment analysis based on the topic keyword is more effective than the existing method and reflects the community environment.
Smart Agent based Dynamic Data Aggregation for Delay Sensitive Smart City Services
Md. Shirajum Munir, Sarder Fakhrul Abedin, Md. Golam Rabiul Alam, Do Hyeon Kim, Choong Seon Hong
http://doi.org/10.5626/JOK.2018.45.4.395
Smart city is the vision of modern intelligent technology toward the sustainable development of green technology and social development. Smart services e.g. smart transportation, smart health, smart home, smart grid, smart security, and IoT based applications are the key enablers of smart city, that ensure the quality life and well-being. In a bid to ensure the functionalities of those services, the IoT applications gather data from numerous IoT nodes. In such a case, it becomes more challenging to managing huge network traffic in the centralized network of smart city. Therefore, in this research, we have focused on the resolution of this problem through the introduction of of smart agent-based dynamic data aggregation (DDA) from distributed dense smart city network for city service fulfillment. In this research study, we purposed to model a peer to peer fully distributed system using distributed hash table chord protocol. We also proposed an algorithm for the IoT network and designed smart agent based IoT node searching algorithm for crowd sourcing. Finally, we simulated the result of the proposed smart agent based dynamic data aggregation model in an effort to achieve a higher performance gain for the proposed approach in respect to service fulfillment time and convergence.
An Optimization Method for Performance Improvement of Set-based Similar Sequence Matching
http://doi.org/10.5626/JOK.2018.45.4.403
The set-based similar sequence matching method involves searching for data set sequences that are similar to a query set sequence. In the method, the similarity between two sets is represented as the size of intersection between them. However, there is a critical performances issue for calculating intersection size if the number of sets is large. In the past, authors of the present work proposed a method to improve the performance of set-based similar sequence matching using simple index structure. In this paper, we propose an optimization method for more efficient running of set-based similar sequence matching. Our method is based on pruning that excludes unnecessary calculation. Through experiments, we show that the proposed method reduces the execution time by about 20% compared to the existing methods.
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