English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90069/105176 (86%)
Visitors : 6369118      Online Users : 582
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112221


    Title: 利用循序樣式探勘強化網路怪客遊戲規則
    Using Sequential Pattern Mining to Enhance iMonsters Game Rules
    Authors: 單柏揚
    SHAN, BO-YANG
    Contributors: 行動商務與多媒體應用學系
    Keywords: 網路安全;遊戲式學習;iMonsters 網路怪客;循序樣式探勘;遊戲歷程
    game-based learning;Sequential Pattern Mining;cyber security;game rule refining algorithm;card game
    Date: 2019
    Issue Date: 2019-10-28
    Publisher: 亞洲大學
    Abstract: 由於物聯網、人工智慧與大數據的迅速發展,資訊安全問題層出不窮,其重要性也逐漸受到重視。國內外已有不少研究已利用遊戲式學習來教導網路安全基本概念與資訊素養且有不錯的成效。亞洲大學知識與資料工程實驗室所開發的「iMonsters網路怪客」桌遊,目前在教學上已有不小的成效,也有不少喜愛的玩家。但藉由收集玩家的回饋意見,從中仍發現了一些問題。本研究想達成的目的即是為了改善這些問題且讓遊戲的遊戲性、知識性與教育性達到一個較好的平衡。本研究提出遊戲規則精煉演算法利用循序樣式探勘強化iMonsters遊戲規則,透過遊戲歷程觀察並發現一些異常,例如: 發現玩家在手牌屬性不均勻的情況下,遊戲無法進行或是玩家之間對於規則的認知一致性不同等問題,來幫助我們依據客觀的資料以精煉遊戲規則。
    我們藉由亞洲大學寒假的兒童網路創意營進行教學與測試,並透過前、後測及問卷訪談,以便我們可以快速修改遊戲規則且從中了解玩家的學習狀況。此外,我們將本研究的方法結合實驗室所提出的攻防知識整理演算法,收集、分析真實案例並導入iMonsters網路怪客桌遊內,當遇到新型態的案例無法結合時,可以透過遊戲規則演化演算法,修改遊戲規則以符合日新月異的網絡攻防手法。
    In the era of the rapid development of IoT, artificial intelligence and big data, Information security issues are on the rise and the importance of cyber security has gradually gained attention. Many studies using game-based learning to teach the cyber security concept and literacy have gained good results. For example, "iMonsters" card game, developed by KDELab of Asia University, currently has been effective in the teaching and has a lot of favorite players. But according to the feedback from the players, there still exist some problems. In this study, we will solve these problems to achieve a better balance between the gameplay, domain knowledge and education. We first proposed the game refining algorithm using Sequential Pattern Mining to strengthen the iMonsters game rules. For example, some anomalies have been found including the existence of the barrier of the game and the different interpretations of the game rules. Therefore, the game rule refining algorithm can help us to refine the game rules.
    We then conducted the teaching and testing of the card game through the winter camp of the Asian University. According to the results of pre-tests, post-tests and questionnaires, we further modified the rules of the game and obtained the players' learning status. In addition, we applied the Internet security knowledge building algorithm proposed by the KDELab to analyze the collected real Internet security incidents and to modify the iMonsters card game rules if the new incidents cannot be solved. Finally the game rules evolution algorithm was proposed to modify the game rules to conform to the ever-changing cyber-attack techniques.
    Appears in Collections:[行動商務與多媒體應用學系] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML6View/Open


    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback