政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119881

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119881

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/119881

Please use this identifier to cite or link to this item: https://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 劉其峰 Liu, Chi-Feng
Keywords:	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
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:	碩士國立政治大學資訊管理學系 105356019
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:	[Department of MIS] Theses

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