Vol. 44, No. 4,
Apr. 2017
Digital Library
Performance Evaluation of Review Spam Detection for a Domestic Shopping Site Application
As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.
Efficient Attribute Based Digital Signature that Minimizes Operations on Secure Hardware
Jungjoon Yoon, Jeonghyuk Lee, Jihye Kim, Hyunok Oh
An attribute based signature system is a cryptographic system where users produce signatures based on some predicate of attributes, using keys issued by one or more attribute authorities. If a private key is leaked during signature generation, the signature can be forged. Therefore, signing operation computations should be performed using secure hardware, which is called tamper resistant hardware in this paper. However, since tamper resistant hardware does not provide high performance, it cannot perform many operations requiring attribute based signatures in a short time frame. This paper proposes a new attribute based signature system using high performance general hardware and low performance tamper resistant hardware. The proposed signature scheme consists of two signature schemes within a existing attribute based signature scheme and a digital signature scheme. In the proposed scheme, although the attribute based signature is performed in insecure environments, the digital signature scheme using tamper resistant hardware guarantees the security of the signature scheme. The proposed scheme improves the performance by 11 times compared to the traditional attribute based signature scheme on a system using only tamper resistant hardware.
An Effective Technique for Protecting Application Data using Security Enhanced (SE) Android in Rooted Android Phones
This paper analyzes security threats in Security Enhanced (SE) Android and proposes a new technique to efficiently protect application data including private information on rooted Android phones. On an unrooted device, application data can be accessed by the application itself according to the access control models. However, on a rooted device, a root-privileged shell can disable part or all of the access control model enforcement procedures. Therefore, a root-privileged shell can directly access sensitive data of other applications, and a malicious application can leak the data of other applications outside the device. To address this problem, the proposed technique allows only some specific processes to access to the data of other applications including private information by modifying the existing SEAndroid Linux Security Module (LSM) Hook function. Also, a new domain type of process is added to the target system to enforce stronger security rules. In addition, the proposed technique separates the directory type of a newly installed application and the directory type of previously installed applications. Experimental results show that the proposed technique can effectively protect the data of each application and incur performance overhead up to or less than 2 seconds.
Consideration of fsync() of the Ext4 File System According to Kernel Version
Seongbae Son, Yoenjin Noh, Dokeun Lee, Sungsoon Park, Youjip Won
Ext4 file system is widely used in various computing environments such as those of the PC, the server, and the Linux-based embedded system. Ext4, which uses a buffer for block I/O, provides fsync() system call to applications to guarantee the consistency of a specific file. A log of the analytical studies regarding the operation of Ext4 and the improvement of its performance has been compiled, but it has not been studied in detail in terms of kernel versions. We figure out that the behavior of fsync() system call is different depending on the kernel version. Between the kernel versions of 3.4.0 and 4.7.2, 3.4.0, 3.8.0, and 4.6.2 showed behavioral differences regarding the fsync() system call. The latency of fsync() in kernel 3.4.0 is longer than that of the more-advanced 3.7.10; meanwhile, the characteristics of 3.8.0 enabled the disruption of the Ext4 journaling order, but the ordered defect was solved with 4.6.2.
Accurate Step-Count Detection based on Recognition of Smartphone Hold Position
Taeho Hur, Haneul Yeom, Sungyoung Lee
As the walking exercise is emphasized in personalized healthcare, numerous services demand walking information. Along with the propagation of smartphones nowadays, many step-counter applications have been released. But these applications are error-prone to abnormal movements such as simple shaking or vibrations; also, different step counts are shown when the phone is positioned in different locations of the body. In this paper, the proposed method accurately counts the steps regardless of the smartphone position by using an accelerometer and a proximity sensor. A threshold is set on each of the six positions to minimize the error of undetection and over-detection, and the cut-off section is set to eliminate any noise. The test results show that the six position type were successfully identified, and through a comparison experiment with the existing application, the proposed technique was verified as superior in terms of accuracy.
Large Scale Incremental Reasoning using SWRL Rules in a Distributed Framework
Wan-Gon Lee, Sung-Hyuk Bang, Young-Tack Park
As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user’s empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.
Matching for the Elbow Cylinder Shape in the Point Cloud Using the PCA
The point-cloud representation of an object is performed by scanning a space through a laser scanner that is extracting a set of points, and the points are then integrated into the same coordinate system through a registration. The set of the completed registration-integrated point clouds is classified into meaningful regions, shapes, and noises through a mathematical analysis. In this paper, the aim is the matching of a curved area like a cylinder shape in 3D point-cloud data. The matching procedure is the attainment of the center and radius data through the extraction of the cylinder-shape candidates from the sphere that is fitted through the RANdom Sample Consensus (RANSAC) in the point cloud, and completion requires the matching of the curved region with the Catmull-Rom spline from the extracted center-point data using the Principal Component Analysis (PCA). Not only is the proposed method expected to derive a fast estimation result via linear and curved cylinder estimations after a center-axis estimation without constraint and segmentation, but it should also increase the work efficiency of reverse engineering.
Neural Theorem Prover with Word Embedding for Efficient Automatic Annotation
Wonsuk Yang, Hancheol Park, Jong C. Park
We present a system that automatically annotates unverified Web sentences with information from credible sources. The system turns to neural theorem proving for an annotating task for cancer related Wikipedia data (1,486 propositions) with Korean National Cancer Center data (19,304 propositions). By switching the recursive module in a neural theorem prover to a word embedding module, we overcome the fundamental problem of tremendous learning time. Within the identical environment, the original neural theorem prover was estimated to spend 233.9 days of learning time. In contrast, the revised neural theorem prover took only 102.1 minutes of learning time. We demonstrated that a neural theorem prover, which encodes a proposition in a tensor, includes a classic theorem prover for exact match and enables end-to-end differentiable logic for analogous words.
Three-Dimensional Conjugate Heat Transfer Analysis for Infrared Target Modeling
Hyunsung Jang, Namkoo Ha, Seungha Lee, Taekyu Choi, Minah Kim
The spectral radiance received by an infrared (IR) sensor is mainly influenced by the surface temperature of the target itself. Therefore, the precise temperature prediction is important for generating an IR target image. In this paper, we implement the combined three-dimensional surface temperature prediction module against target attitudes, environments and properties of a material for generating a realistic IR signal. In order to verify the calculated surface temperature, we are using the well-known IR signature analysis software, OKTAL-SE and compare the result with that. In addition, IR signal modeling is performed using the result of the surface temperature through coupling with OKTAL-SE.
Rank Correlation Coefficient of Energy Data for Identification of Abnormal Sensors in Buildings
Naeon Kim, Sihyun Jeong, Boyeon Jang, Chong-Kwon Kim
Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building’s HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.
A Video Quality Control Scheme Based on the Segment Characteristics to Improve the QoE for HTTP Adaptive Streaming (HAS) Services
Recently, the video quality control schemes for the improvement of the QoE (Quality of Experience) of video streaming services that are based on DASH (Dynamic Adaptive Streaming over HTTP), which is a standard of HTTP adaptive streaming (HAS) services, have been studied. However, the problem of the existing schemes is the degradation that is due to unnecessary quality changes because the VBR (Variable Bitrate) characteristics of the video are not considered. In this paper, we propose a SC-DASH (Segment Characteristics-based DASH) which controls the video quality based on the segment characteristics. The SC-DASH can prevent the occurrence of the unnecessary quality changes by controlling the video quality based on the size of the next segment, the segment throughput, and the buffer occupancy. The experiment results showed that the SC-DASH improves the QoE by reducing the unnecessary quality changes compared with the existing quality control schemes.
A Traffic-Classification Method Using the Correlation of the Network Flow
YoungHoon Goo, Kyuseok Shim, Sungho Lee, Baraka D. Sija, MyungSup Kim
Presently, the ubiquitous emergence of high-speed-network environments has led to a rapid increase of various applications, leading to constantly complicated network traffic. To manage networks efficiently, the traffic classification of specific units is essential. While various traffic-classification methods have been studied, a methods for the complete classification of network traffic has not yet been developed. In this paper, a correlation model of the network flow is defined, and a traffic-classification method for which this model is used is proposed. The proposed network-correlation model for traffic classification consists of a similarity model and a connectivity model. Suggestion for the effectiveness of the proposed method is demonstrated in terms of accuracy and completeness through experiments.
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