政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125535

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125535

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政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/125535

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题名:	filterNN: 基於神經網路之序列資料特徵選取方法 filterNN: NN-based Feature Selection from Sequential Data
作者:	吳皓銘 Wu, Hao-Ming
贡献者:	蕭舜文 Hsiao, Shun-Wen 吳皓銘 Wu, Hao-Ming
关键词:	神經網路特徵擷取序列資料 Neural Network Feature extraction Feature selection Sequential data
日期:	2019
上传时间:	2019-09-05 15:45:56 (UTC+8)
摘要:	我們設計了一個新的神經網絡架構，它由兩部分組成，過濾器和分類器。我們實現了三種過濾器，可以過濾掉不必要的輸入數據。過濾後的數據將被輸入後一種分類器，以實現最高的訓練精度。由於過濾器和分類器一起訓練，因此，過濾器將保持輸入，這有助於分類器執行分類。因此，剩餘的輸入數據可以被視為該類的特徵。我們還設計了三個成本函數來實現不同的目的，1）濾波輸入可以盡可能少，2）濾波輸入可以更連續，3）分類器可以實現最高的訓練精度。這三個學習目標相互衝突，因此我們在本研究中展示了調整過程以實現最佳性能。我們使用基於文本的順序數據來測試所提出的神經網路架構的有用性。使用基於文本的順序數據是從現實世界收集的惡意軟件執行API調用。研究表明，所提出的神經網路架構有助於處理基於文本的序列數據，並將過濾域專家的特徵以進行進一步分析。 We design a new Neural Network architecture which consists of two parts, filter, and classifier. We implement three kinds of filters, which can filter out unnecessary input data. The filtered data will be fed into the latter classifier to achieve the highest training accuracy. Because of the filter and classifier are trained together, thus, the filter will keep the inputs which help classifier to perform the classification. Therefore, the remaining input data can be viewed as the characteristic of the class. We also design three cost function to achieve different purpose, 1) the filtered inputs could be as less as possible, 2) the filtered inputs could be more consecutive as possible, 3) the classifier could achieve the highest training accuracy as possible. The three learning goals are in conflict with each other, so we demonstrate the tuning process in this research to achieve the best performance. We use text-based sequential data to test the usefulness of the proposed NN architecture. The use of text-based sequential data is malware execution API calls which are collected from the real world. The research shows that the proposed NN architecture is helpful for dealing with text-based sequential data and will filter the characteristic for domain experts to perform further analyze.
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Schmidhuber, “Stacked convolutional auto-encoders for hierarchical feature extraction,” in International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 2011, pp. 52-59. [26] “Malware Knowledge Base”, Owl.nchc.org.tw, 2018. [Online]. Available: https://owl.nchc.org.tw/. [Accessed: 14- Apr- 2018]. [27] “VirusTotal”, Virustotal.com, 2018. [Online]. Available: https://www.virustotal.com/zh-tw/. [Accessed: 14- Apr- 2018]. [28] “Build a Convolutional Neural Network using Estimators — TensorFlow”, TensorFlow, 2019. [Online]. Available: https://www.tensorflow.org/tutorials/layers. [Accessed: 24- Feb- 2019]. https://www.tensorflow.org/tutorials/layers [29] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097-1105. [30] U. Bioinformatics Laboratory, “Orange Data Mining Fruitful & Fun”, Orange.biolab.si, 2019. [Online]. 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描述:	碩士國立政治大學資訊管理學系 106356005
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G1063560051
数据类型:	thesis
DOI:	10.6814/NCCU201900745
显示于类别:	[資訊管理學系] 學位論文

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