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    题名: 歸納惡意軟體特徵
    Malware Family Characterization
    作者: 劉其峰
    Liu, Chi-Feng
    贡献者: 郁方
    Yu, Fang
    劉其峰
    Liu, Chi-Feng
    关键词: 遞歸神經網路
    增長層級式自我組織映射圖
    長短期記憶
    惡意軟體
    動態分析
    序列編碼
    RNN
    GHSOM
    LSTM
    Malware
    Sequence encoding
    Dynamic analysis
    日期: 2018
    上传时间: 2018-09-03 15:47:50 (UTC+8)
    摘要: 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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    描述: 碩士
    國立政治大學
    資訊管理學系
    105356019
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0105356019
    数据类型: thesis
    DOI: 10.6814/THE.NCCU.MIS.025.2018.A05
    显示于类别:[資訊管理學系] 學位論文

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