• International Journal of Advanced Culture Technology
• /
• v.8 no.2
• /
• pp.239-245
• /
• 2020

With the recent revised curriculum, the importance of exploring children's play through new teaching media is increasing in kindergarten. In this study, it is to use the robot 'Bee-bot' for early children to uncover the changes that children have through free exploration and play. As a result of comparing the change of scientific problem-solving ability of 3, 4, and 5-year-olds, there were significant changes in all three sub-elements. We propose to us scientific problem-solving ability test tools, propose and apply ideas for problem-solving, conclusion on problem-solving Building. Through this, it was found that unplugged play using 'Bee-bot' is meaningful as a play environment and as a teaching medium for children aged 3, 4 and 5 years old.

• ETRI Journal
• /
• v.35 no.6
• /
• pp.1058-1067
• /
• 2013

An approach for game bot detection in massively multiplayer online role-playing games (MMORPGs) based on the analysis of game playing behavior is proposed. Since MMORPGs are large-scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. To cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.2
• /
• pp.225-238
• /
• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• ETRI Journal
• /
• v.45 no.4
• /
• pp.713-723
• /
• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.3
• /
• pp.422-427
• /
• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1097-1104
• /
• 2021

Online game-bots are already known for a lot of persons by various ways. It leads to problems such as declining game player's interest, in-game financial crisis, etc. Detecting and restricting of game-bot is now essential. Because both publishers and players get disadvantages from their long term abnormal working. But it is not easy to restrict, because of false restriction risks. Game publishers need to distinguish game-bot from server-side game logs. At last, it should can make reasons for game-bot restriction. In this paper, we classified game-bot users by using daily separated game logs for testing data. For daily-driven detection, we separated total dataset into one day logs. Preliminary detects game-bots with one day logs, and determines total results by using these data. Daily driven detection advantages on detection which contains combined game playing style. Which shows like normal user and game-bot. These methodology shows better F1-score, which one of indicator which demonstrate classification accuracy. It increases from 0.898 to 0.945 by using Random Forest classifier.

• Journal of Practical Engineering Education
• /
• v.16 no.2
• /
• pp.105-114
• /
• 2024

Recently, with an emphasis on software proficiency, universities are providing software education to all students regardless of their majors. However, non-majors often lack motivation for software education and perceive the unfamiliar learning content as more challenging. To address this issue, tailored software education according to the learners' characteristics is essential. Art students, for instance, with their strong visual comprehension and expressive abilities, can benefit from utilizing visual literacy to enhance the effectiveness of programming education. In this study, we propose decomposing everyday problems into flowcharts and pseudocode to construct procedural and visual images. Using the educational programming language PlayBot, we aim to analyze the effectiveness of teaching by coding to solve problems. Through this approach, students are expected to grasp programming concepts, understand problem-solving processes through computational thinking, and acquire skills to apply programming in their respective fields.

• The Journal of Korean Academy of Sensory Integration
• /
• v.14 no.1
• /
• pp.9-18
• /
• 2016

Objective : The current study investigated the effectiveness of group occupational activity program in increasing visual-perception and motor function of children in Community Children Center. Methods : Five children aged between 6-9 years in a community children center participated in the group occupational activity program. The program was designed to facilitate children's visual-perception and motor function based on play occupations. We examined performances of MVPT-3 and BOT-2 before and after the program. Results : Children who participated in the group program showed significant increases in visual perception and motor function. Conclusion : The study revealed the effectiveness of group occupational activity program in promoting visual perception and motor function of children in a community children center, which suggests the possibility of application of occupational activity toward low-income children in the community.

• Journal of the Korea Society for Simulation
• /
• v.28 no.1
• /
• pp.23-39
• /
• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.