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研究生:施崑哲
研究生(外文):Kuen-Jhe Shih
論文名稱:探勘使用者擊鍵分群以提昇自由文辨識準確度之研究
論文名稱(外文):Mining a Novel Biometrics to Improve the Accuracy of Personal Authentication in Free Text
指導教授:蔡政容蔡政容引用關係
指導教授(外文):Cheng-Jung Tsai
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:數學系所
學門:數學及統計學門
學類:數學學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:38
中文關鍵詞:資料探勘群集分析生物特徵擊鍵特徵自由文分類器
外文關鍵詞:data miningclusteringbioinformaticskeystroke dynamicsfree textclassifier
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電腦網路為人們的生活帶來了許多便利,但同時也提供了電腦病毒快速且便捷的散播管道。因人們在網路上無法確認彼此真實身分以及帳號密碼易被破解、盗用等問題,使得近年來網路犯罪的事件層出不窮。近年來,有學者將擊鍵特徵運用至自由文辨識,相關研究結果顯示擊鍵特徵確實能提昇自由文之辨識率。為了提昇自由文之辨識準確性,本論文藉由資料探勘中的群集分析技術分析使用者的擊鍵方式,建構出新的生物特徵「擊鍵鍵盤分群圖」(Keystroke Clusters Map ),簡稱KC-Map。因KC-Map是群集分析後之結果,已非使用者真實的擊鍵資料,所以並不適用於一般的分類器。為解決這個問題,本論文亦提出一個適用於KC-Map的分類器,稱之為「擊鍵鍵盤分群圖相似性分類器」(Keystroke Clusters Map Similarity Classifier),簡稱KCMS分類器。實驗結果顯示, 結合KC-Map與KCMS分類器可改善自由文辨識的準確性,其準確度提昇達1.27倍。
此外,目前針對自由文的相關研究,皆須要使用者經過數個月以上的擊鍵訓練,在實用性上有非常大的問題。本論文之另一動機,在探討若將訓練時間縮短在使用者可接受之範圍,自由文辨識系統是否能達到良好的辨識率。實驗結果顯示,在提升自由文辨識系統實用性之前題下,使用者僅須進行約20分鐘的訓練,自由文辨識即可達到良好的辨識準確度。

Internet brings people lots of conveniences, but it provides a mode which can spread virus of computer easily and quickly. There are some problems can be showed; for example, people cannot identify personal details to each other accurately on Internet. Also, username as well as password are easily cracked or embezzled. Therefore, cybercrime is highly increased. Recently, some scholars draw keystroke dynamics on free text identification; some relevant researches show that the keystroke dynamics can really improve the accuracy of personal authentication in free text. In order to improve the accuracy of personal authentication in free text, this study proposes a new biometrics referred to KC-Map (Keystroke Clusters Map) by clustering users’ keystrokes. Because KC-Map is the results of clustering, the user’s keystrokes have been non-informed. Therefore, KC-Map is not suitable for the traditional statistical classifier, which is used for authentication. In order to solve this problem, the study also proposes a KCMS classifier (Keystroke Clusters Map Similarity Classifier). Experimental results show that combination of KC-Map and KCMS classifier can improve the accuracy of personal authentication in free text with up to 1.27 times.
In addition, there is a big problem on the current free text identification that users are required to be trained for several months. Long training time makes free text identification impractical. Another motivation of this study is to explore if it is possible to shorten the training time in an acceptable range. Experimental results show that users need to carry out only about 20 minutes for training to achieve a good identification accuracy.

目錄
摘要 I
Abstract II
目錄 III
表次 IV
圖次 V
第1章 緒論 1
第2章 相關研究 5
2.1 資料探勘(Data Mining)與群集分析(Clustering Analysis) 5
2.2 生物特徵(Biometrics) 6
2.3 擊鍵特徵(Keystroke Dynamics) 6
2.4 自由文辨識 9
第3章 方法 13
3.1 系統架構 13
3.2 註冊階段 14
3.3 探勘「擊鍵鍵盤分群圖」 16
3.4 統計分類器 19
3.5 擊鍵鍵盤分群圖相似性分類器 21
3.6 使用者驗證 23
第4章 實驗結果與討論 25
第5章 結論與未來研究方向 31
參考文獻 32

表次
表3.1、(a)模擬使用者在註冊時收集到之PR擊鍵資料,其中”null”未知資料; (b)根據(a)建構出之KC-Map 18
表3.2、(a)本論文之實驗中某一使用者在註冊時之PR擊鍵資料,其中”null”未知資料,時間單位為ms; (b)根據(a)建構出之KC-Map 19
表3.3、合法使用者f1與任一使用者f2的KC-Map 23
表4.1、本論文100位實驗者的基本資料統計表 26
表4.2、The EER with the most balanced optimum threshold of our system 27
表4.3、與相關文獻之比較,其中 ”-” 表示該研究未詳細說明 28

圖次
圖2.1、各項擊鍵特徵示意圖 8
圖2.2、常見的三種效能評估方法關係圖 9
圖2.3、排序法舉例圖[14] 11
圖3.1、本論文提出之自由文KDA系統之架構流程圖 14
圖3.2、本論文開發之自由文KDA系統的註冊界面 15
圖3.3、根據表3.2 (b)之KC-Map繪製出之示意圖,不同顏色代表不同群集 19
圖4.1、針各種不同分群數與不同的KC-thr值之分析比較圖 28
圖4.2、固定KC-thr為0.73下,分群數目k與EER之關係圖 29
圖4.3、分群數目之ROC比較圖 29
圖4.4、僅使用擊鍵特徵與採用擊鍵特徵並結合KC-Map和KCMS分類器之ROC比較圖 30


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