Vol. 45, No. 3,
Mar. 2018
Digital Library
Secure MQTT Protocol based on Attribute-Based Encryption Scheme
http://doi.org/10.5626/JOK.2018.45.3.195
Recently, with increasing scale of internet of Things (IoT), a large amount of data are generated and various services using such data are emerging. Therefore, a protocol suitable for IoT environment that can efficiently process / transmit big data is needed. MQTT is a lightweight messaging protocol for IoT environment. Although MQTT protocol can use TLS to provide security, it has a problem in that handshake and packet overhead will increase when TLS is used. Therefore, this paper proposed as Secure_MQTT protocol. It can provide stronger security by using lightweight encryption algorithm for MQTT protocol.
Experimental Analysis of Recent Works on the Overlap Phase of De Novo Sequence Assembly
Jihyuk Lim, Sun Kim, Kunsoo Park
http://doi.org/10.5626/JOK.2018.45.3.200
Given a set of DNA read sequences, de novo sequence assembly reconstructs a target sequence without a reference sequence. For reconstruction, the assembly needs the overlap phase, which computes all overlaps between every pair of reads. Since the overlap phase is the most time-consuming part of the whole assembly, the performance of the assembly depends on that of the overlap phase. There have been extensive studies on the overlap phase in various fields. Among them, three state-of-the-art results for the overlap phase are Readjoiner, SOF, and Lim-Park algorithm. Recently, a rapid development of sequencing technology has made it possible to produce a large read dataset at a low cost, and many platforms for generating a DNA read dataset have been developed. Since the platforms produce datasets with different statistical characteristics, a performance evaluation for the overlap phase should consider datasets with these characteristics. In this paper, we compare and analyze the performances of the three algorithms with various large datasets.
Direction Relation Representation and Reasoning for Indoor Service Robots
Seokjun Lee, Jonghoon Kim, Incheol Kim
http://doi.org/10.5626/JOK.2018.45.3.211
In this paper, we propose a robot-centered direction relation representation and the relevant reasoning methods for indoor service robots. Many conventional works on qualitative spatial reasoning, when deciding the relative direction relation of the target object, are based on the use of position information only. These reasoning methods may infer an incorrect direction relation of the target object relative to the robot, since they do not take into consideration the heading direction of the robot itself as the base object. In this paper, we present a robot-centered direction relation representation and the reasoning methods. When deciding the relative directional relationship of target objects based on the robot in an indoor environment, the proposed methods make use of the orientation information as well as the position information of the robot. The robot-centered reasoning methods are implemented by extending the existing cone-based, matrix-based, and hybrid methods which utilized only the position information of two objects. In various experiments with both the physical Turtlebot and the simulated one, the proposed representation and reasoning methods displayed their high performance and applicability.
A Study on Distributed Parallel SWRL Inference in an In-Memory-Based Cluster Environment
Wan-Gon Lee, Seok-Hyun Bae, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.3.224
Recently, there are many of studies on SWRL reasoning engine based on user-defined rules in a distributed environment using a large-scale ontology. Unlike the schema based axiom rules, efficient inference orders cannot be defined in SWRL rules. There is also a large volumet of network shuffled data produced by unnecessary iterative processes. To solve these problems, in this study, we propose a method that uses Map-Reduce algorithm and distributed in-memory framework to deduce multiple rules simultaneously and minimizes the volume data shuffling occurring between distributed machines in the cluster. For the experiment, we use WiseKB ontology composed of 200 million triples and 36 user-defined rules. We found that the proposed reasoner makes inferences in 16 minutes and is 2.7 times faster than previous reasoning systems that used LUBM benchmark dataset.
Mobile Gamer Categorization with Archetypal Analysis and Cognitive-Psychological Features from Log Data
Jihoon Jeon, Dumim Yoon, Seongil Yang, Kyungjoong Kim
http://doi.org/10.5626/JOK.2018.45.3.234
The study of classifying gamer types or analyzing the characteristics of gamers is a field of interest for data analysis researchers. From the past to the present, much research has been done on gamer categorization and gamer analysis. However, most studies use surveys or bio-signals, which is not practical because it is difficult to obtain large amounts of data. Even if the game log is used, it is difficult to analyze the psychology of the gamer because the gamer is categorized and analyzed by extracting only statistical values. However, if we can extract the cognitive psychology information of the gamer from the basic game log, we can analyze the gamer more intuitively and easily. In this paper, we extracted eight cognitive psychological features representing the behavior and psychological information of the gamer using Crazy Dragon"s game log, which is a mobile Role-Playing-Game (RPG). In addition, we classified gamers based upon cognitive psychological features and analyzed them using eight cognitive psychological features. As a result, most gamers were highly correlated with one or two types.
A Defect Management Process based on Open Source Software for Small Organizations
http://doi.org/10.5626/JOK.2018.45.3.242
For high-quality software development, it is necessary to detect and fix the defects inserted. If defect management activities are not properly performed, it will lead to the project delay and project failure due to rework. Therefore, organizations need to establish defect management process and institutionalize it. Process standard models handle defect management in the area of project monitoring and control. However, small organizations experience difficulties in implementing and applying defect management process in a real situation. In this paper, we propose a defect management process for small organization which is designed in accordance with the characteristics of a small projects such as few participants and short development period. The proposed defect management process will be based on a tool chain with open source software such as Redmine, Subversion, Maven, Jenkins that support a defect management process and SW Visualization in systematic way. We also proposed a way of constructing defect database and various methods of analyzing and controlling defect data based on it. In an effort to prove the effectiveness of the proposed process, we applied the process and tool chain to a small organization.
An Integrated Model of Cybersickness: Understanding User’s Discomfort in Virtual Reality
Eunhee Chang, Daeil Seo, Hyun Taek Kim, Byounghyun Yoo
http://doi.org/10.5626/JOK.2018.45.3.251
Users can experience cybersickness when interacting with virtual reality (VR). The symptoms of cybersickness are similar to those of motion sickness which include eye fatigue, disorientation, and nausea. Despite the longstanding interest of user’s discomfort, inconsistent results have been drawn on the underlying mechanisms and solutions of cybersickness. In this study, we propose an integrated view of cybersickness connecting causes of the symptoms, human perception model, and measurements of cybersickness. Cybersickness-related factors of previous research are reorganized into content, hardware, and human factors as well as analyzed in terms of VR fidelity. Also, pros and cons that measure the degree of cybersickness are discussed.
S-PARAFAC: Distributed Tensor Decomposition using Apache Spark
Hye-Kyung Yang, Hwan-Seung Yong
http://doi.org/10.5626/JOK.2018.45.3.280
Recently, the use of a recommendation system and tensor data analysis, which has high-dimensional data, is increasing, as they allow us to analyze the tensor and extract potential elements and patterns. However, due to the large size and complexity of the tensor, it needs to be decomposed in order to analyze the tensor data. While several tools are used for tensor decomposition such as rTensor, pyTensor, and MATLAB, since such tools run on a single machine, they are unable to handle large data. Also, while distributed tensor decomposition tools based on Hadoop can handle a scalable tensor, its computing speed is too slow. In this paper, we propose S-PARAFAC, which is a tensor decomposition tool based on Apache Spark, in distributed in-memory environments. We converted the PARAFAC algorithm into an Apache Spark version that enables rapid processing of tensor data. We also compared the performance of the Hadoop based tensor tool and S-PARAFAC. The result showed that S-PARAFAC is approximately 4～25 times faster than the Hadoop based tensor tool.
Differentially Private k-Means Clustering based on Dynamic Space Partitioning using a Quad-Tree
Hanjun Goo, Woohwan Jung, Seongwoong Oh, Suyong Kwon, Kyuseok Shim
http://doi.org/10.5626/JOK.2018.45.3.288
There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker’s background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.
Anomaly Detection Analysis using Repository based on Inverted Index
Jumi Park, Weduke Cho, Kangseok Kim
http://doi.org/10.5626/JOK.2018.45.3.294
With the emergence of the new service industry due to the development of information and communication technology, cyber space risks such as personal information infringement and industrial confidentiality leakage have diversified, and the security problem has emerged as a critical issue. In this paper, we propose a behavior-based anomaly detection method that is suitable for real-time and large-volume data analysis technology. We show that the proposed detection method is superior to existing signature security countermeasures that are based on large-capacity user log data according to in-company personal information abuse and internal information leakage. As the proposed behavior-based anomaly detection method requires a technique for processing large amounts of data, a real-time search engine is used, called Elasticsearch, which is based on an inverted index. In addition, statistical based frequency analysis and preprocessing were performed for data analysis, and the DBSCAN algorithm, which is a density based clustering method, was applied to classify abnormal data with an example for easy analysis through visualization. Unlike the existing anomaly detection system, the proposed behavior-based anomaly detection technique is promising as it enables anomaly detection analysis without the need to set the threshold value separately, and was proposed from a statistical perspective.
Link Performance Analysis of LoRa for Real-time Information Gathering in Maritime Conditions
Jaeho Shin, Junyeong Lim, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2018.45.3.303
LoRaWAN(Long Range Wide Area Network) is a standard for low-power, long-range, low-speed communication as announced in the LoRa Alliance. LoRaWAN addresses the physical layer and medium access control layer and the technology used in the physical layer is referred to as LoRa. LoRa can be used for remote monitoring and remote control in maritime conditions. However, unlike land, marine environment is not only difficult to construct an infrastructure for service provision, but also difficult to analyze LoRa performance in maritime. In this study, we construct an infrastructure using cloud platform and analyze LoRa link performance in maritime conditions.
ENF based Detection of Forgery and Falsification of Digital Files due to Quadratic Interpolation
http://doi.org/10.5626/JOK.2018.45.3.311
Recently, the use of digital audio and video as proof in criminal and all kinds of litigation is increasing, and scientific investigation using digital forensic technique is developing. With the development of computing and file editing technologies, anyone can simply manipulate video files, and the number of cases of manipulating digital data is increasing. As a result, the integrity of the evidence and the reliability of the evidence Is required. In this paper, we propose a technique for extracting the Electrical Network Frequency (ENF) through a grid of power grids according to the geographical environment for power supply, and then performing signal processing for peak detection using QIFFT. Through the detection algorithm using the standard deviation, it was confirmed that the video file was falsified with 73% accuracy and the forgery point was found.
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