• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1077-1084
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• 2015

Security issues such as an illegal acquisition of personal information and identity theft happen due to using game bots in online games. Game bots collect items and money unfairly, so in-game contents are rapidly depleted, and honest users feel deprived. It causes a downturn in the game market. In this paper, we defined the growth types by analyzing the growth processes of users with actual game data. We proposed the framework that classify hard-core users and game bots in the growth patterns. We applied the framework in the actual data. As a result, we classified five growth types and detected game bots from hard-core users with 93% precision. Earlier studies show that hard-core users are also detected as a bot. We clearly separated game bots and hard-core users before full growth.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1153-1160
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• 2018

As the number of online game users increases and the market size grows, various kinds of cheating are occurring. Game bots are a typical illegal program that ensures playtime and facilitates account leveling and acquisition of various goods. In this study, we propose a method to detect game bots based on user action time interval (ATI). This technique observes the behavior of the bot in the game and selects the most frequent actions. We distinguish between normal users and game bots by applying Machine Learning to feature frequency, ATI average, and ATI standard deviation for each selected action. In order to verify the effectiveness of the proposed technique, we measured the performance using the actual log of the 'Aion' game and showed an accuracy of 97%. This method can be applied to various games because it can utilize all actions of users as well as character movements and social actions.

• Bulletin of the Korean Chemical Society
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• v.34 no.12
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• pp.3629-3634
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• 2013

In this study, an improved method for the quantitative analysis of ginkgolic acids (GAs) in Ginkgo biloba leaf extract was developed. The samples were extracted with a mixture of chloroform and 50 % ethanol, after which the chloroform extract was dried and reconstituted in methanol. GAs with 13:0, 15:1, and 17:1 in the extract were successfully separated within 40 min and determined with high throughput performance using an online column-switching HPLC method using an SP column C8 SG80 ($4.6{\times}150mm$, $5{\mu}m$) and a Cadenza 5CD C18 column ($4.6{\times}150mm$, $3{\mu}m$). The developed HPLC method was validated for Ginkgo biloba leaf extract. The validation parameters were specificity, linearity, precision, accuracy, and limits of detection and quantitation (LODs and LOQs, respectively). It was found that all of the calibration curves showed good linearity ($r^2$ > 0.9993) within the tested ranges. The LODs and LOQs were all lower than $0.04{\mu}g/mL$. The established method was found to be simple, rapid, and high throughput for the quantitative analysis of GAs in ten commercial Ginkgo biloba leaf extract and dietary supplements. The samples were also analyzed in LC-electrospray ionization (ESI) tandem mass spectrometry (MS/MS) - multiple-ion reaction monitoring (MRM) mode to confirm the identification results that were obtained by the column switching HPLC-DAD method. The developed method is considered to be suitable for the routine quality control and safety assurance of Ginkgo biloba leaf extract.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.72-79
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• 2006

Recently, a MMORPG(Massively Multi-play Online Role Playing Game) has built distribute server by Seamless world. This paper proposes an efficient collision detection method. DLS is used to dynamically adjust spatial subdivisions in each the boundary regions of distribute server We use an index table to effectively utilize the relationships between in the nodes and can perform the collision detection efficiently by reconstructing nodes of the tree. Also, we maintain the information for the boundary region to efficiently detect the collections and adjust the boundary regions between distributed servers by using DLS. As the DLS uses pointers, the information for each server is not needed and the boundary regions between the distributed servers are efficiently searched. Using node index points, the construction table can be made to find between ray and neighborhood node, In addition, processes for Network traffic reduce because a copy of the boundary regions is not needed when a object moves with realtime.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1415-1425
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• 2018

MMORPG genre is based on the belief that all users in virtual world are equal. All users are able to obtain the corresponding wealth or status as they strive under the same resource, time. However, game bot is the main factor for harming this fair competition, causing benign gamers to feel a relative deprivation and deviate from the game. Game bots mainly form GFG(Gold Farming Group), which collects the goods in the game indiscriminately and adversely affects the economic system of the game. A general game bot detection algorithm is useful for detecting each bot, but it only covers few portions of GFG, not the whole, so it needs a wider range of detecting method. In this paper, we propose a method of detecting GFG based on items used in MMORPG genre. Several items that are mainly traded in the game were selected and the flows of those items were represented by a network. We Identified the characteristics of exchanging items of GFG bots and can identify the GFG's item trade network with real datasets from one of the popular online games.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8495-8501
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• 2016

Purpose: This study was conducted to assess the agreement and differences between cervical self-sampling with a Kato device (KSSD) and gynecologist sampling for Pap cytology and human papillomavirus DNA (HPV DNA) detection. Materials and Methods: Women underwent self-sampling followed by gynecologist sampling during screening at two primary health clinics. Pap cytology of cervical specimens was evaluated for specimen adequacy, presence of endocervical cells or transformation zone cells and cytological interpretation for cells abnormalities. Cervical specimens were also extracted and tested for HPV DNA detection. Positive HPV smears underwent gene sequencing and HPV genotyping by referring to the online NCBI gene bank. Results were compared between samplings by Kappa agreement and McNemar test. Results: For Pap specimen adequacy, KSSD showed 100% agreement with gynecologist sampling but had only 32.3% agreement for presence of endocervical cells. Both sampling showed 100% agreement with only 1 case detected HSIL favouring CIN2 for cytology result. HPV DNA detection showed 86.2%agreement (K=0.64, 95% CI 0.524-0.756, p=0.001) between samplings. KSSD and gynaecologist sampling identified high risk HPV in 17.3% and 23.9% respectively (p=0.014). Conclusion: The self-sampling using Kato device can serve as a tool in Pap cytology and HPV DNA detection in low resource settings in Malaysia. Self-sampling devices such as KSSD can be used as an alternative technique to gynaecologist sampling for cervical cancer screening among rural populations in Malaysia.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.142-149
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• 2014

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.

• Convergence Security Journal
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• v.22 no.5
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• pp.11-20
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• 2022

As the Internet and web technology develop around mobile devices, image data contains various types of sensitive information such as people, text, and space. In addition to these characteristics, as the use of SNS increases, the amount of damage caused by exposure and abuse of personal information online is increasing. However, research on de-identification technology based on multi-type object detection for personal information protection is insufficient. Therefore, this paper proposes an artificial intelligence model that detects and de-identifies multiple types of objects using existing single-type object detection models in parallel. Through cutmix, an image in which person and text objects exist together are created and composed of training data, and detection and de-identification of objects with different characteristics of person and text was performed. The proposed model achieves a precision of 0.724 and mAP@.5 of 0.745 when two objects are present at the same time. In addition, after de-identification, mAP@.5 was 0.224 for all objects, showing a decrease of 0.4 or more.