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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/119881
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/119881

    Title: 歸納惡意軟體特徵
    Malware Family Characterization
    Authors: 劉其峰
    Liu, Chi-Feng
    Contributors: 郁方
    Yu, Fang
    Liu, Chi-Feng
    Keywords: 遞歸神經網路
    Sequence encoding
    Dynamic analysis
    Date: 2018
    Issue Date: 2018-09-03 15:47:50 (UTC+8)
    Abstract: Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attracts hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, we adopt a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105356019
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MIS.025.2018.A05
    Appears in Collections:[資訊管理學系] 學位論文

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