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Thesis Andrew 2009.Pdf 誌誌誌謝謝謝 0林'S上的0P9I，¨¨ú動，(ir的Y¤<-，/一**高'的û¨GG， «不知名的力Ϩ得IJI的，6W老F與iP，(七月的!園áb閒逛，}Ig(臉 上±£生¼，XY¤的福#，B¼有_會悠閒0G³(圖ø(MË9他?8的x!，( 還沒¦法分¨左右i邊/ê個û的û(KM，J，我要bm了，/r感謝>º的B候0 了。 ͪ'º: 這~t都沒}}0j¨，'疚多些。 i華老+: /}}老+，沒有>#，可以Í受我'個x期êú現bM一!。 昇K老+: 真可惜沒0J2"m。（z<）（v而還/會想1|º寬T老+）。 z冠王=: 感謝v初/持我2修。 鈊aûPÊ/舞團隊: Ð供J2的ø關Ç訊。 $#)的'%們: I¬、懷寧、s鳳、ø賢、冠華...，(這~t``們吃吃喝喝。 同x們: ``們*G的TS，可以開懷¢笑，``們H通的消o，可以順)bm。 最後r感謝的/，(«邊IJI的波波還有Y¤，讓我知S揮W如è的七月H後，_ 可以有甜eÃ-的®笑。 i Contents 誌謝 ....................................... i -文X要 ..................................... v ñ文X要 ..................................... vii 1 Introduction 1 2 Related Work 4 3 FPS Game Bot Detection 6 3.1 FPS Games . 6 3.2 Quake 2 and Its Game Bots . 6 3.3 Data Collection . 7 3.3.1 Human Traces . 7 3.3.2 Bot Traces . 8 3.3.3 Trace Summary . 9 3.4 Discriminative Analysis . 9 3.4.1 Aggregated Navigation Pattern . 10 3.4.2 Individual Trajectories . 12 3.5 Bot Detection Scheme . 13 3.5.1 Feature Extraction . 13 3.5.2 Classi¯cation . 19 3.6 Performance Evaluation . 19 4 Rhythm Game Bot Detection 21 4.1 Rhythm Games . 21 4.2 Dancing Online . 21 4.2.1 Normal Mode . 22 4.2.2 Rhythm Mode . 23 4.2.3 Beat Mode . 24 4.3 Dance Online Game Bots . 24 4.4 Data Description . 24 4.5 The Recording Program . 25 4.5.1 Data Declaration . 25 4.5.2 Recording Program Design . 28 4.5.3 Data Collection . 29 4.6 Discriminative Analysis . 33 4.7 Bot Detection Scheme . 35 4.7.1 Time Spent Based . 35 4.7.2 Pressure-Error Based . 37 4.8 Performance Evaluation . 37 ii 4.8.1 Time Spent Based . 37 4.8.2 Pressure-Error Based . 38 5 Conclusion 42 References 45 iii List of Figures 3.1 A screen shot of Maze War. 7 3.2 A screen shot of Quake 2. 8 3.3 Presence locations of all players. 10 3.4 Game play trajectories of a human player and bot players. 12 3.5 The distribution of features related to ON/OFF periods. 14 3.6 The distribution of features related to movement pace. 15 3.7 The distribution of features related to movement path. 17 3.8 The distribution of features related to turn movement. 18 3.9 Classi¯cation accuracy between human and bots. 20 3.10 Classi¯cation accuracy between four types of players (human and three bot programs). 20 4.1 The screen shot and dance platform of Dance Dance Revolution. 22 4.2 A normal mode screen shot of Dancing Online. 22 4.3 A rhythm mode screen shot of Dancing Online. 23 4.4 A beat mode screen shot of Dancing Online. 23 4.5 The setup dialog of DCO. 25 4.6 Texture of Dancing Online. 29 4.7 The time relationship between player's traces and standard keys. 32 4.8 The probability distribution of 16 combinations. 35 4.9 Interval time to error rate of 16 arrow combinations in players' traces. 36 4.10 16 Types of Players' Conditional Probability Distribution. 38 4.11 Player's Consistency Analysis. 39 4.12 Bot with Normal distribution. 40 4.13 Quanti¯cation of Pressures. 40 4.14 Players' Error Rate under Di®erent Pressures. 41 iv List of Tables 3.1 Trace summary . 9 4.1 An example of Ti time stamp . 30 4.2 An example of player' trace . 31 4.3 Game experience of players . 31 4.4 Traces summary . 33 v ---文文文XXX要要要 近t來，多º線上J2成º了²路上^8nM的休閒;動，F¨W多º線上J2的蓬 Ã|U，©¶:P的Lº_變得越來越8見，v-最´Í的，要算/使(俗1 bot 的ê 動化_hº程式了，原因/ bot 使(者不需付ú對應的ª力，s可r得不合Å理的N õ，一,而言，J2>¤間/不認同 bot 的X(的。6而想要¨X bot 使(者，6後 加以Í6/困ã的，原因/ bot ,來1/«-計來(uªJ2規G下!仿©¶Lº， vLº!式與真c©¶ø仿。以往J2經營者嘗f以肉<u,J2_hº程式的¹式， 經8"生¤$H例糾紛可見一,，因d目MJ2"m期望藉©一些軟體技S，嘗f來u , bot 的X(，以增加$定的準º率。這些u,技S'致上有~.^型，,一./(J 22L-，-·©¶2L¹式2而分析©¶^型，:點/r擾©¶2LJ2的順¢。, 二.Í6¹式/(J22L-，ã,ûq/&有 bot 程式X(，這.¹式MÐ/G- _hº程式/Å須依D¼¢6端J2程式，且多Jê針對y定功能者。例如 FPS J2 -，專ø瞄準的_hº程式，v:點/1»一,'。因d(,論文-，我們對i.線上 J2^型分%Ðú對應的 bot u,¹式。 ,一.º俗1 FPS 的,一º1射ÊJ2，Ðúú¼J2-ºiû動軌á的¹法u , FPS J2-的_hº程式，它可以«應(¼@有w有û動軌á^型J2的一,'技 S。透N真實©¶x據分析，我們得知，真實©¶û動的軌áy'與£些_hº程式/ ^8不同的，雖6J2_hº程式竭力!仿真實©¶的Lº，F/因º©¶Lº/^8 ã以仿 ，這點/我們的;要理論ú礎。我們¡(了 Quake 2 \º實際研vH例，9據 U0真實©¶的紀錄o:，這.u,¹法(以 200 秒º一觀,®M下，可以T0 95% 以上的準º'。 ,二.針對節O^型J2，Ðú一.ú¼壓力與/¤率的¹法來u,跳舞_^型J2 的_hº程式。它_/一個可以«應(¼d^跳舞_^型J2的一,'技S，理論ú礎 vi /，因º©¶會¨W節Íëb與按uD合b成的壓力'小，與按uB間與按u<與ú/ 的_率成c比，_因Ç料!®，因dd^ bot ^8¹易透Nx習真實©¶的¹式，|U Í6 bot u,的技S。6而，d^ bot 得知我們的xx式實際Ãx<，&GÍ6我們的 u,，_/A分困ã的。我們¡(了/舞h尊d>多º線上J2\º我們的實際研vH 例，1¼至目Mºb尚*~0對d^型J2 bot u,的論文，因d我們/,一個可以( 來u,節O^型 bot 的論文。 關關關uuu^^^: :PLºu,, 線上J2, 安h', 分^, ©¶Lº vii ñññ文文文XXX要要要 In recent years, online game has become one of the most popular Internet ac- tivities, but cheating activity, such as game bots, has increased as a consequence. Generally, the gamer community disagrees with the use of game bots, as bot users obtain unreasonable rewards without corresponding e®orts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either interrupt the players' gaming experiences, or assume game bots are run as standalone clients or assigned a speci¯c goal, such as aim bots in FPS games. Therefore, we separately propose game bot detection approaches according to two di®erent types of online games. 1) A trajectory-based approach to detect FPS game bots. It is a general tech- nique that can be applied to any game in which the avatar's movement is controlled directly by the players. Through real-life data traces, the result shows that the trajectories of human players and those of game bots are very di®erent. In addition, although game bots may endeavor to simulate players' decisions, certain human be- havior patterns are di±cult to mimic because they are AI-hard. Taking Quake 2 as a case study, we evaluate our scheme's performance based on real-life traces. The result shows that the scheme can achieve a detection accuracy to 95% or higher, given a trace of 200 seconds or longer. 2) A pressure-error-based approach to detect rhythm game bots. It is also a general technique that can be applied to any rhythm game in which the errors increase the dependance on pressure from the game speed and key combinations. viii Theoretically, the bot can ¯ght back by learning the real player's behaviors, but without our arguments of formula, the bot is hard to do that. The study case is Dancing Online. Up to now, we have not found any paper about rhythm game bots detection, so we believe that this paper is the ¯rst one for rhythm game bots detection. Key words: Cheating Detection, Online Games, Quake, Dancing Online, Secu- rity, Supervised Classi¯cation, User Behavior ix Chapter 1 Introduction In recent years, online game has become one of the most popular Internet ac- tivities. However, as the population of online gamers has increased, game cheating problems, such as the use of game bots, have become more and more serious. Game bots are automated programs with arti¯cial intelligence for players to use on di®er- ent purposes. In MMORPGs (Massively Multiplayer Online Role Player Games), players can save a great deal of time by using bots to perform repetitive tasks, such as slashing low-level monsters, or ¯shing in a river to master the avatar's ¯shing skills. In FPS (First-Person Shooter) games and rhythm games, users can employ bots to play in place of themselves in order to get high scores and gain a reputation in the community. Generally, the gamer community disagrees with the use of game bots, as bot users obtain unreasonable rewards without corresponding e®orts. However, game bots are hard to detect because they are designed to simulate human game playing behaviors and they follow the game rules exactly. Some bot detection studies [7,19] propose that using CAPTCHA tests during a game can determine whether an avatar is actually controlled by a person or not. Although it is e®ective, it interrupts the game and degrades players' feelings of immersion in the virtual world [10,14]. Alter- natively, passive detection approaches, such as schemes based on tra±c analysis [1,2] and schemes based on avatars' shooting accuracy in FPS games [20], can be used. The former approach assumes that a game bot works as a standalone client, and the 1 latter is only valid for detecting aim bots in shooting games. The genre of MMOG is very varied, so we only select two very popular types of online games and try to propose the general approaches separately to detect each bots. 1) A trajectory-based approach for all genres of games where a player controls the avatar's movement directly which is based on the avatar's movement trajectory during a game. 2) A pressure-error-based approach for rhythm games where a player follows rhythm to hit the corresponding key which is based on the pressure and error rate relationship of the player during a game. The rationale of the ¯rst approach is that the trajectory of the avatar controlled by a human player is hard to simulate.
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