• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.267-274
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• 2021

This study used data from the 6rd and 12th year of the Korean Welfare Panel to evaluate the effects of the Personal Assistance Service(PAS) system on the labor market of PAS users' participation. For the purpose of this study, 64 program groups using the Korean PAS and 344 control groups not using the Korean PAS were selected using Caliper matching among the propensity score matching. A chi-square test was used for the difference in characteristics between groups, and a simple difference-in-differences (DID) model and a double-difference multiple regression analysis of DID were performed to estimate the effect of thepolicy before and after the Korean PAS. As a result of the study, it was found that statistically, PAS had no significant effect on the labor market. This is due to the low number of system users, resulting in low post-hoc power, incomplete matching and limited availability of PAS Assistants for Disabled People. Therefore, In order to demonstrate the effectiveness of the Personal Assistance Service(PAS) system, specialized services and systems that meet the needs of the disabled and household members should be implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.325-333
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• 2021

With the rapidly changing era of the fourth industrial revolution, the utilization of IT technology is increasing. In addition, the demand for security authentication is increasing as shared services or IoT technologies are being developed as new business models. Security authentication is becoming increasingly important for all intelligent devices such as self-driving cars. However, most location-based security authentication technologies are being developed mainly with technologies that utilize server proximity or satellite location tracking, which limits the scope of their physical use. Location-based security authentication technology has recently been developed as a complementary replacement technology. In this study, we introduce location-based security authentication technology using cell broadcasting technology, which has a wider range of applications and is more convenient and business-friendly than existing location-based security authentication technologies. We also introduced application cases and business models related to this. In addition to the current status of technology development, we analyzed current changes in business models being employed. Based on our analysis results, this study draws the implication that technology diversification is necessary to improve the performance of innovative technologies. It is meaningful that it has found and studied advanced technologies other than existing location authentication methods and systems.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.29-41
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• 2022

In this paper, we introduce a novel real-time, interactive animation system which uses real-time motion inputs from a low-cost motion-sensing device Kinect. Our system generates interaction motions between the user character and the counterpart character in real-time. While the motion of the user character is generated mimicking the user's input motion, the other character's motion is decided to react to the user avatar's motion. During a pre-processing step, our system analyzes the reference motion data and generates mapping model in advance. At run-time, our system first generates initial poses of two characters and then modifies them so that it could provide plausible interacting behavior. Our experimental results show plausible interacting animations in that the user character performs a modified motion of user input and the counterpart character properly reacts against the user character. The proposed method will be useful for developing real-time interactive animation systems which provide a better immersive experience for users.

• Ocean and Polar Research
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• v.44 no.4
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• pp.269-285
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• 2022

The interaction between ocean and ice shelf is a critical physical process in relation to water mass transformations and ice shelf melting/freezing at the ocean-ice interface. However, it remains challenging to thoroughly understand the process due to a lack of observational data with respect to ice shelf cavities. This is the first study to simulate the variability and circulation of water mass both overlying the continental shelf and underneath an ice shelf and an ice tongue in the Terra Nova Bay (TNB), East Antarctica. To explore the properties of water mass and circulation patterns in the TNB and the corresponding effects on sub ice shelf basal melting, we explicitly incorporate the dynamic-thermodynamic processes acting on the ice shelf in the Regional Ocean Modeling System. The simulated water mass formation and circulation in the TNB region agree well with previous studies. The model results show that the TNB circulation is dominated by the geostrophic currents driven by lateral density gradients induced by the releasing of brine or freshwater at the polynya of the TNB. Meanwhile, the circulation dynamics in the cavity under the Nansen Ice shelf (NIS) are different from those in the TNB. The gravity-driven bottom current induced by High Salinity Shelf Water (HSSW) formed at the TNB polynya flows towards the grounding line, and the buoyance-driven flow associated with glacial meltwater generated by the HSSW emerges from the cavity along the ice base. Both current systems compose the thermohaline overturning circulation in the NIS cavity. This study estimates the NIS basal melting rate to be 0.98 m/a, which is comparable to the previously observed melt rate. However, the melting rate shows a significant variation in space and time.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Conservation Science
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• v.37 no.6
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• pp.626-637
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• 2021

The tomb complex of the royal family from the period of the Ungjin Baekje Kingdom (475 to 538 AD) in Gongju, Korea, contains the tomb of King Muryeong and other royal tombs. After the excavation of the tomb of King Muryeong in 1971, these tombs were opened up to the public, without the establishment of systems for their safety, conservation and management. The tombs have consequently experienced rapid environmental changes and suffered various damages. In this study, specific vulnerable parts inside the tombs were selected for deviation analysis using 3D scanning, and 3D image models were constructed on this basis. Progressive displacement was identified in tomb No. 5, and basic data for future investigations was acquired from tomb No. 6 and the tomb of King Muryeong. In the deviation analysis for the southern plastered wall of tomb No. 5, the damage was not found to exceed the ranges of ±18 mm and ±2 mm. However, the lintel stone was found to be sagging by 0.32 mm on average, and the distance between the walls to have increased by 0.36 mm on average. Direct water seepage occurring in tomb No. 5 is considered to be increasing the damage within the tomb, such as the dropping and sagging of the lintel. The 3D image models constructed in this study will play an important role as baseline data for future research, and can be used to discuss a secure conservation scheme for the tombs through cross-validation with precise measurement monitoring.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.