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Chatting Pattern Based Game BOT Detection: Do They Talk Like Us?
( Ah Reum Kang ),( Huy Kang Kim ),( Jiyoung Woo ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.11
Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot`s playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers` communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.
Kang, Ah-Reum,Ahn, Sung-Won,Lee, Seong-Jae,Lee, Byung-Hwan,Lee, Sung-Sik,Kim, Ju-Min 한국유변학회 2011 Korea-Australia rheology journal Vol.23 No.4
Medium viscoelasticity contributes to the lateral particle migration and particle segregation under channel flows. It is recently reported that the equilibrium positions of the particles in dilute suspensions are greatly affected in a rectangular microchannel by the relative ratio of the medium viscoelastic and inertial forces (Yang et al., 2011). Here, we summarize the forces which may exert on the particles in concentrated suspensions under the viscoelastic flows including inertial lift force, and the shear-induced migration effect arising from particle-particle interaction. In addition, we report some preliminary results regarding particle segregation in concentrated suspensions (i.e., ${\phi}_{bulk}=0.05$, 0.1) under both inertial and inertialess viscoelastic flows in the rectangular microchannel. Similar to our previous results in dilute suspensions (Yang et al., 2011), the particles tend to migrate toward low first normal stress regions, and the particle migration is greatly affected by existence of inertia. We firstly report the formation of multi-layered particle trains along the corners in this work. These results give an insight that the medium viscoelascticity would contribute to the design of particle separation devices in complex suspensional flows (e.g. blood cell separation), and also the understanding of particle dynamics in confined environments under viscoelastic flows.
Effect of genetic background differences between FVB and C57BL/6 mice in SARS-CoV-2 infection
Ah-Reum Kang,Hyun Ah Noh,Jae Hyung Son,Sun-Min Seo,Ji-Hun Lee,Na-Won Kim,Eun-Seon Yoo,Han-Bi Jeong,Da In On,Ji Yun Jang,Jun-Won Yun,Jun Won Park,Kang-Seuk Choi,Ho-Young Lee,Jun-Young Seo,Ki Taek Nam,J 한국실험동물학회 2022 한국실험동물학회 학술발표대회 논문집 Vol.2022 No.7
문서 구조 및 스트림 오브젝트 분석을 통한 문서형 악성코드 탐지
강아름(Ah Reum Kang),정영섭(Young-Seob Jeong),김세령(Se Lyeong Kim),김종현(Jonghyun Kim),우지영(Jiyoung Woo),최선오(Sunoh Choi) 한국컴퓨터정보학회 2018 韓國컴퓨터情報學會論文誌 Vol.23 No.11
In recent years, there has been an increasing number of ways to distribute document-based malicious code using vulnerabilities in document files. Because document type malware is not an executable file itself, it is easy to bypass existing security programs, so research on a model to detect it is necessary. In this study, we extract main features from the document structure and the JavaScript contained in the stream object In addition, when JavaScript is inserted, keywords with high occurrence frequency in malicious code such as function name, reserved word and the readable string in the script are extracted. Then, we generate a machine learning model that can distinguish between normal and malicious. In order to make it difficult to bypass, we try to achieve good performance in a black box type algorithm. For an experiment, a large amount of documents compared to previous studies is analyzed. Experimental results show 98.9% detection rate from three different type algorithms. SVM, which is a black box type algorithm and makes obfuscation difficult, shows much higher performance than in previous studies.
강아름 ( Ah Reum Kang ),이승국 ( Seung Kuk Lee ),김상완 ( Sang Wan Kim ) 대한원격탐사학회 2013 大韓遠隔探査學會誌 Vol.29 No.4
공간해상도 약 1 m의 고해상도 X-band SAR 위성이 이용되면서 SAR를 이용한 도심지 모니터링, 표적탐지, 건물 재구성에 관한 연구가 진행되고 있다. 본 연구에서는 고해상도 TerraSAR-X SAR 영상을 이용한 도심지 건물 재구성을 수행하였다. 도심지 건물 재구성을 위하여 1:25,000 수치지형도로부터 건물의 외곽선을 추출하였으며, 추출한 건물의 외곽선을 기반으로 SAR 영상에서 모서리반사 위치를 찾았다. KS 테스트(Kolmogorov-Smirnov Test)에 기반하여 고해상도 SAR 진폭영상의 건물 모서리반사 위치로부터 레이오버 길이를 측정하여 건물의 초기 높이를 설정하였다. 진폭영상을 이용하여 추출한 건물의 초기 높이 기준 -10 m에서 +10 m로 건물의 높이를 변화시키며 도심지에 적합한 간섭위상 시뮬레이션을 수행하여 TerraSAR-X 간섭위상과의 위상 일치성 계산을 하였다. 위상 일치의 경향성 분석을 통해 건물의 높이를 설정해 줌으로써 고해상도 SAR 영상을 이용한 도심지 건물 재구성 연구를 진행하였다. 대전지역의 아파트 단지에 적용한 결과, 진폭영상과 간섭위상을 이용하여 추정된 건물 높이는 LiDAR로부터 추출된 높이를 기준으로 약 1~2 m 정도의 RMSE (Root Mean Square Error)를 보였다. 개발된 알고리즘은 향후 TerraSARX와 TanDEM-X 간섭쌍 자료에 적용할 경우, 보다 도심지 모니터링에 효과적으로 이용될 수 있을 것이다. The monitoring of urban area, target detection and building reconstruction have been actively studied and investigated since high resolution X-band SAR images could be acquired by airborne and/or satellite SAR systems. This paper describes an efficient approach to reconstruct artificial structures (e.g. apartment, building and house) in urban area using high resolution X-band SAR images. Building footprint was first extracted from 1:25,000 digital topographic map and then a corner line of building was detected by an automatic detecting algorithm. With SAR amplitude images, an initial building height was calculated by the length of layover estimated using KS-test (Kolmogorov-Smirnov test) from the corner line. The interferometric SAR phases were simulated depending on SAR geometry and changable building heights ranging from -10 m to +10 m of the initial building height. With an interferogram from real SAR data set, the simulation results were compared using the method of the phase consistency. One of results can be finally defined as the reconstructed building height. The developed algorithm was applied to repeatpass TerraSAR-X spotlight mode data set over an apartment complex in Daejeon city, Korea. The final building heights were validated against reference heights extracted from LiDAR DSM, with an RMSE (Root Mean Square Error) of about 1~2m.
강아름(Ah Reum Kang),우지영(Jiyoung Woo),박주용(Juyong Park),김휘강(Huy Kang Kim) 한국정보보호학회 2012 정보보호학회논문지 Vol.22 No.2
온라인 게임의 다양한 보안 위협 가운데, 온라인 게임 봇의 사용이 게임 서비스에 가장 심각한 문제를 야기하고 있다. 본 논문에서는 온라인 게임 봇 탐지를 위한 소셜 액티비티 분석 프레임워크를 제안한다. 이 프레임워크를 이용하여 게이머의 소셜 액티비티를 가장 많이 포함하고 있는 파티 플레이(party play) 로그를 분석하는 데에 적용하였다. 게임 봇은 일반 사용자들과 다르게, 사이버 자산을 빠르게 얻는데 특화되어 있기 때문에 소셜 액티비티를 분석할 경우 정상적인 사용자들과 행동 패턴에 차이가 있다. 이 특징을 이용하여 게임 봇 이용자와 일반 이용자들을 구분해 낼 수 있도록, 사용자 행위를 분석하고 온라인 게임 봇 탐지를 위한 임계값을 정의하였다. 탐지 규칙을 포함하는 지식 기반 시스템을 구축한 뒤 이를 국내 최대, 세계 6위 규모의 게임에 적용하였다. 본 논문의 프레임워크를 활용하여 분류를 한 결과 95.92%의 높은 정확도를 보였다. 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%.