Vol. 45, No. 1,
Jan. 2018
Digital Library
Intelligent Malicious Web-page Detection System based on Real Analysis Environment
Jongseok Song, Kyeongsuk Lee, Wooseung Kim, Ikkyoon Oh, Yongmin Kim
http://doi.org/10.5626/JOK.2018.45.1.1
Recently, distribution of malicious codes using the Internet has been one of the most serious cyber threats. Technology of malicious code distribution with detection bypass techniques has been also developing and the research has focused on how to detect and analyze them. However, obfuscated malicious JavaScript is almost impossible to detect, because the existing malicious code distributed web page detection system is based on signature and another limitation is that it requires constant updates of the detection patterns. We propose to overcome these limitations by means of an intelligent malicious code distributed web page detection system using a real browser that can analyze and detect intelligent malicious code distributed web sites effectively.
qtar: Design and Implementation of an Optimized tar Command with FTL-level Remapping
Jeongseok Ryoo, Sangwook Shane Hahn, Jihong Kim
http://doi.org/10.5626/JOK.2018.45.1.9
Tar is a Linux command that combines several files into a single file. Combining multiple small files into large files increases the compression efficiency and data transfer speed. However, tar has a problem in that smaller target files, result in a lower performance. In this paper, we show that this performance degradation occurs when tar reads the data from the target files and propose qtar (quick tar) to solve this problem via flash-level remapping. When the size of an I/O request is less than 1 MB, the I/O performance decreases proportionally to the decrease in size of the I/O request. Since tar reads the data of files one by one, a smaller file size results in a lower performance. Therefore, the remapping technique is implemented in qtar to read data from the target files at the maximum I/O size regardless of the size of each file. Our evaluations show that the execution time with qtar is reduced by up to 3.4 times compared to that with tar.
Graph Construction Based on Fast Low-Rank Representation in Graph-Based Semi-Supervised Learning
http://doi.org/10.5626/JOK.2018.45.1.15
Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph – based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.
Extracting Rules from Neural Networks with Continuous Attributes
Batselem Jagvaral, Wan-Gon Lee, Myung-joong Jeon, Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.1.22
Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.
Analyzing and Solving GuessWhat?!
Sang-Woo Lee, Cheolho Han, Yujung Heo, Wooyoung Kang, Jaehyun Jun, Byoung-Tak Zhang
http://doi.org/10.5626/JOK.2018.45.1.30
GuessWhat?! is a game in which two machine players, composed of questioner and answerer, ask and answer yes-no-N/A questions about the object hidden for the answerer in the image, and the questioner chooses the correct object. GuessWhat?! has received much attention in the field of deep learning and artificial intelligence as a testbed for cutting-edge research on the interplay of computer vision and dialogue systems. In this study, we discuss the objective function and characteristics of the GuessWhat?! game. In addition, we propose a simple solver for GuessWhat?! using a simple rule-based algorithm. Although a human needs four or five questions on average to solve this problem, the proposed method outperforms state-of-the-art deep learning methods using only two questions, and exceeds human performance using five questions.
Model-based Specification of Non-functional Requirements in the Environment of Real-time Collaboration Among Multiple Cyber Physical Systems
http://doi.org/10.5626/JOK.2018.45.1.36
Due to the advent of the 4th Industrial Revolution, it is imperative that we aggressively continue to develop state-of-the-art, cutting edge ICT technology relative to autonomous vehicles, intelligent robots, and so forth. Especially, systems based on convergence IT are being developed in the form of CPSs (Cyber Physical Systems) that interwork with sensors and actuators. Since conventional CPS specification only expresses behavior of one system, specification for collaboration and diversity of CPS systems with characteristics of hyper-connectivity and hyper-convergence in the 4th Industrial Revolution has been insufficiently presented. Additionally, behavioral modeling of CPSs that considers more collaborative characteristics has been unachieved in real-time application domains. This study defines the non-functional requirements that should be identified in developing embedded software for real-time constrained collaborating CPSs. These requirements are derived from ISO 25010 standard and formally specified based on state-based timed process. Defined non-functional requirements may be reused to develop the requirements for new embedded software for CPS, that may lead to quality improvement of CPS.
Design of EEG Signal Security Scheme based on Privacy-Preserving BCI for a Cloud Environment
Kwon Cho, Donghyeok Lee, Namje Park
http://doi.org/10.5626/JOK.2018.45.1.45
With the advent of BCI technology in recent years, various BCI products have been released. BCI technology enables brain information to be transmitted directly to a computer, and it will bring a lot of convenience to life. However, there is a problem with information protection. In particular, EEG data can raise issues about personal privacy. Collecting and analyzing big data on EEG reports raises serious concerns about personal information exposure. In this paper, we propose a secure privacy-preserving BCI model in a big data environment. The proposed model could prevent personal identification and protect EEG data in the cloud environment.
SWOSpark : Spatial Web Object Retrieval System based on Distributed Processing
Pyoung Woo Yang, Kwang Woo Nam
http://doi.org/10.5626/JOK.2018.45.1.53
This study describes a spatial web object retrieval system using Spark, an in - memory based distributed processing system. Development of social networks has created massive amounts of spatial web objects, and retrieval and analysis of data is difficult by using exist spatial web object retrieval systems. Recently, development of distributed processing systems supports the ability to analyze and retrieve large amounts of data quickly. Therefore, a method is promoted to search a large-capacity spatial web object by using the distributed processing system . Data is processed in block units, and one of these blocks is converted to RDD and processed in Spark. Regarding the discussed method, we propose a system in which each RDD consists of spatial web object index for the included data, dividing the entire spatial region into non-overlapping spatial regions, and allocating one divided region to one RDD. We propose a system that can efficiently use the distributed processing system by dividing space and increasing efficiency of searching the divided space. Additionally by comparing QP-tree with R-tree, we confirm that the proposed system is better for searching the spatial web objects; QP-tree builds index with both spatial and words information while R-tree build index only with spatial information.
Identification of Heterogeneous Prognostic Genes and Prediction of Cancer Outcome using PageRank
http://doi.org/10.5626/JOK.2018.45.1.61
The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.
Incremental Linear Discriminant Analysis for Streaming Data Using the Minimum Squared Error Solution
Gyeong-Hoon Lee, Cheong Hee Park
http://doi.org/10.5626/JOK.2018.45.1.69
In the streaming data where data samples arrive sequentially in time, it is difficult to apply the dimension reduction method based on batch learning. Therefore an incremental dimension reduction method for the application to streaming data has been studied. In this paper, we propose an incremental linear discriminant analysis method using the least squared error solution. Instead of computing scatter matrices directly, the proposed method incrementally updates the projective direction for dimension reduction by using the information of a new incoming sample. The experimental results demonstrate that the proposed method is more efficient compared with previously proposed incremental dimension reduction methods.
Protocol Analysis and Evaluation of the Transport Layer to Improve Security in a Public Cloud Environment
Jin Sook Bong, Sang Jin Park, Yongtae Shin
http://doi.org/10.5626/JOK.2018.45.1.76
Governments and public agencies try to use the cloud to carry out their work and provide public services. However, a public cloud is vulnerable to security side because it has a structure to support services using public networks (i.e, the internet). Thus, this paper finds the general security vulnerabilities of a network and compares and analyzes the characteristics of transport protocols (UDP, TCP, SCTP, and MPTCP) on the basis of their security vulnerabilities. This paper uses a reliability and security factor for the comparative analysis, evaluates the security exposure, and chooses a suitable protocol considering the security of the transport protocols in the cloud circumstance.
Video Quality Control Scheme for Efficient Bandwidth Utilization of HTTP Adaptive Streaming in a Multiple-Clients Environment
Minsu Kim, Heekwang Kim, Kwangsue Chung
http://doi.org/10.5626/JOK.2018.45.1.86
When multiple clients share bandwidth and receive a streaming service, HTTP Adaptive Streaming has a problem in that the bandwidth is measured inaccurately due to the ON-OFF pattern of the segment request. To solve the problem caused by the ON-OFF pattern, the proposed PANDA (Probe AND Adapt) determines the quality of the segment to be requested while increasing the target bandwidth. However, since the target bandwidth is increased by a fixed amount, there is a problem in low bandwidth utilization and a slow response to changes in bandwidth. In this paper, we propose a video quality control scheme that improves the low bandwidth utilization and slow responsiveness of PANDA. The proposed scheme adjusts the amount of increase in the target bandwidth according to the bandwidth utilization after judging the bandwidth utilization by comparing the segment download time and the request interval. Experimental results show that the proposed scheme can fully utilize the bandwidth and can quickly respond to changes in bandwidth.
A Deep Neural Network Architecture for Real-Time Semantic Segmentation on Embedded Board
http://doi.org/10.5626/JOK.2018.45.1.94
We propose Wide Inception ResNet (WIR Net) an optimized neural network architecture as a real-time semantic segmentation method for autonomous driving. The neural network architecture consists of an encoder that extracts features by applying a residual connection and inception module, and a decoder that increases the resolution by using transposed convolution and a low layer feature map. We also improved the performance by applying an ELU activation function and optimized the neural network by reducing the number of layers and increasing the number of filters. The performance evaluations used an NVIDIA Geforce GTX 1080 and TX1 boards to assess the class and category IoU for cityscapes data in the driving environment. The experimental results show that the accuracy of class IoU 53.4, category IoU 81.8 and the execution speed of 640x360, 720x480 resolution image processing 17.8fps and 13.0fps on TX1 board.
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