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研究生:黃暄雅
論文名稱:基於腦波與鍵擊的連續型驗證研究
論文名稱(外文):A Study of Continuous Authentications via EEG and Keystroke Dynamics
指導教授:林土量
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:腦波連續型驗證One Class SVM整體學習法
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近年來,以生物特徵為基礎的相關身份驗證研究日益增加,早期多以外部生物特徵為研究基礎,但其被仿冒的風險較內部生物特徵高,因此研究方向已從外部生物特徵轉向內部生物特徵。本研究選用的內部生物特徵為腦波,腦波具有不易取得性與獨特性,能有效避免仿冒問題,且其具有的連續性,可進行使用者的連續驗證。在傳統腦波實驗中,會藉由指定受測者想像部分身體的運動狀態,來取得腦波資料並分析,但由於無法得知受測者是否有遵照實驗內容做正確想像,因此本研究利用鍵擊的輔助,來強化取得的腦波資料,並與透過朗讀強化腦波資料之方法做比較,研究結果顯示選擇使用鍵擊來強化腦波的效果是較好的。此外本研究有將蒐集到的腦波資料做特定區段的擷取分析,在和無處理腦波資料的傳統腦波擷取方法比較下,研究結果顯示有進行特定腦波區段分析的效果是較好的。
本研究選擇單一類別支援向量機 (One Class Support Vector Machine, OC-SVM) 來進行腦波資料的連續性驗證,OC-SVM只需要合法使用者的資料即可進行訓練。在資料擷取的處理階段,會利用鍵擊分析的斷詞輔助,選取需要的腦波資料做訓練。在分類器建構階段會針對不同斷詞的腦波分別建立分類器,得到各斷詞的分類結果後,會根據整體學習法的合併法則多數決法,判斷受測者的身分為合法使用者或非法使用者。研究結果顯示,將利用鍵擊強化的腦波資料,使用多斷詞的分類器搭配整體學習法的合併法則多數決法,在10位受測者的實驗情境下,能達到83% 的正確率。
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第二章 文獻探討 4
第一節 腦波種類 4
第二節 腦波驗證 5
第三節 支援向量機(Support Vector Machine, SVM) 7
第四節 單一類別支援向量機(One Class SVM, OC-SVM)10
第五節 整體學習法(Ensemble Learning) 11
第三章 實驗方法 13
第一節 方法描述 13
第二節 方法流程 16
第三節 特徵值萃取階段 17
第四節 分類器建構階段 21
第五節 比較方法 25
第四章 實驗結果 29
第五章 結論 43
參考文獻 44
附錄一 實驗文章 47
附錄二 完整實驗數據 49
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