基於非侵入式手機使用者識別機制即時檢測結合收斂方法收集使用者操作行為資料;Active Learning with Convergent Stop Rule to Collect User’s Behavior for Training Model Base on Non-intrusive Smartphone Authentication

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/72635

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72635

Title:	基於非侵入式手機使用者識別機制即時檢測結合收斂方法收集使用者操作行為資料;Active Learning with Convergent Stop Rule to Collect User’s Behavior for Training Model Base on Non-intrusive Smartphone Authentication
Authors:	潘珮玟;Pan, Pei-Wen
Contributors:	資訊工程學系
Keywords:	非侵入式識別機制;使用者識別;主動學習;支持向量機
Date:	2016-10-07
Issue Date:	2017-01-23 17:09:17 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著科技進步，人們可使用智慧型手機進行收發電子信箱、線上交易(網路銀行、電子商務)、社群軟體等功能，也使智慧型手機內部資料成為有心人士覬覦的目標。　　目前智慧型手機識別機制有侵入式與非侵入式兩種。傳統的驗證機制（密碼鎖、圖形鎖）屬於侵入式識別機制(一次驗證)。非侵入式識別機制則不需要驗證介面，而是從背景收集使用者行為進行驗證。　　目前文獻上已有提出非侵入式識別機制數種研究，但在訓練樣本數目上仍有需求過多的問題，導致實際應用將會耗費使用者許多時間在提供訓練樣本上。　　本研究提出利用既有主動學習即時檢測收集方法搭配支持向量機選擇訓練樣本，在識別效果接受範圍內以少量的訓練樣本建構非侵入式識別機制。　　本研究提出改善既有主動學習方法的停止條件及新樣本情境檢測方式，分析使用者Validation Accuracy曲線，增加一收斂停止方法，並改用支持向量機超平面最近距離點情境分析。最後本論文提出的改善後即時檢測收集方法與其原先比較，實驗結果則能於相同識別效果下，減少一半的訓練資料收集時間。;In order to protect the data within the smartphone, intrusive and non-intrusive user authentication mechanisms were developed. Traditional authentication mechanisms like number lock and pattern lock are intrusive user authentication mechanism. Non-intrusive user authentication mechanism doesn’t require any user interface, but collect user’s behavior in the background and authenticate it. Several non-intrusive authentication mechanisms were proposed, but them still have problem needed too much data for training. Actually user to provide the training samples can be very time-consuming. This study proposes a method to collect real-time detection with the use of active learning support vector machine choose training samples to identify the effect of the acceptable range in a small amount of training samples construction of non-invasive identification mechanism. For this study, we propose new stopping rule and model analysis with proposed active learning method. We analyze line of SVM validation accuracy, add a new stopping rule with convergence, and replace support vector with the closest sample points as standard for model analysis. Finally, this study presents an optimal method to collect real-time detection (active learning) compared with old version, results are total reduce half of the training time with a same recognition results of old version.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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