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研究生:林哲緯
論文名稱:適用於動態環境中偵測離群值之決策支援機制
論文名稱(外文):A Decision Support Mechanism for Outlier Detection in the Concept Drifting Environment
指導教授:蔡瑞煌蔡瑞煌引用關係
指導教授(外文):Tsaih, Rua Huan
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊管理研究所
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:103
語文別:英文
論文頁數:71
中文關鍵詞:離群值偵測概念飄移移動視窗神經網路決策支援
外文關鍵詞:outlier detectionconcept driftingmoving windowneural networksdecision support
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  • 被引用被引用:0
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近來,偵測離群值已成為一個重要且具有挑戰性的研究議題。從給定之觀察值中我們可以推導出一個適配函數(fitting function),並依照距離此適配函數之距離決定出離群值(outlier)。而此議題在現今的環境中,更為困難:因現今之資料來源多為動態性且不穩定的環境,造成現在的資料具有概念飄移(concept drifting)之特性。
因此本研究提出一個創新的決策支援機制,幫助決策者於動態環境且具概念飄移的特性之資料偵測出離群值。具體而言,本研究希望在網路安全的領域,透過推導出的決策支援機制找出潛在的異常或具攻擊的行為。
本研究推導出的決策支援機制具有下列特點:
(1)使用自適應的單一隱藏層倒傳遞神經網路(single-hidden layer feed-forward neural networks, SLFN)來實作出穩健學習(resistant learning)之概念;
(2)透過移動視窗(moving window)機制實現增量學習(incremental learning)之策略;
(3)兼具效率及效能的決策支援:具備良好的偵測結果,且僅列舉出少量的潛在離群值給決策者。
此研究同時具有實驗進行驗證,實驗結果顯示此決策支援機制是非常具有前途的。

Outliers are observations far away from the fitting function that is deduced from the bulk of the given observations. Recently, to detect them has become an important issue. Since the data nature in the current era has become more concept-drifting, the outlier detection has become more challenging. To address this challenging issue, this study develops a decision support mechanism (DSM) for coping with the outlier detection problem in the concept-drifting environment. Specifically, this study wants to derive a DSM for identifying the potential intrusion detection in network security. The proposed DSM has the following features: (1) the implementation of the resistant learning concept via the adaptive single-hidden layer feed-forward neural networks, (2) the implementation of the incremental learning concept via the moving window technique, and (3) the efficiency and effectiveness in terms of having to review a much less amount of sample and getting a better accuracy of outlier detection. An experiment is designed to justify the proposed DSM. Experiment results show that the performance of proposed DSM is very promising.
FIGURE INDEX 6
TABLE INDEX 7
CHAPTER 1 INTRODUCTION 8
1.1 Background and Motivation 8
1.2 Research Question 10
1.3 Research Method 10
1.4 Purpose and Contribution 11
1.5 Content Organization 13
CHAPTER 2 LITERATURE REVIEW 14
2.1 Concept Drifting 14
2.2 Outlier Detection 20
2.3 Envelope Module 26
2.4 Moving Window 30
2.5 Zero-Day Attack 32
CHAPTER 3 THE PROPOSED DECISION SUPPORT MECHANISM 34
CHAPTER 4 EXPERIMENT DESIGN AND RESULTS 41
4.1 Experiment Design 41
4.2 Performance Evaluation 46
CHAPTER 5 CONCLUSION &; FUTURE WORK 62
REFERENCE 66

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