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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/136843
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136843


    Title: 以成對共識分數評估多個分類器中含雜亂標籤的分類結果
    Evaluate the Classification Result with Cacophonous Labels of Multiple Classifiers by Pairwise Consensus Score
    Authors: 陳璿心
    Chen, Hsuan-Hsin
    Contributors: 蕭舜文
    Hsiao, Shun-Wen
    陳璿心
    Chen, Hsuan-Hsin
    Keywords: 基因演算法
    惡意程式標籤
    惡意程式家族
    Malware labeling
    AV labels
    Malware family
    Date: 2021
    Issue Date: 2021-09-02 15:53:16 (UTC+8)
    Abstract: 在資訊安全的領域中,有許多惡意軟體分類器,這些分類器的目的是給予不同惡意軟體其家族名稱。然而這些家族名稱不像是在做圖形辨識,例如判斷手寫數字的標籤是基於事實,而這些惡意程式家族是基於不同觀點給予不同的標籤。
    我們想知道哪一種觀點是被大眾所接受,所以發展一個不同於多數決的投票方法,而是採用一次比較一對分類器中的一對惡意軟體,並從每一對分類器中加總計算不同對惡意軟體之間的共識分數,最後這些分數就會成為我們判斷獲得最多大眾觀點的依據。此外建立在成對的共識分數機制上,我們另外採用了基因演算法,設法交換出具有最高分數的分類結果,成為在分類惡意軟體的結果可依循的答案。
    除了設計演算法來尋找受到較多支持的惡意軟體偵測廠商外,本研究也嘗試使用三種不同來源的惡意程式資料,並加入經基因演算法取得的最佳解來計算每個來源個別的共識分數,並證明取得的最佳解經過交換後分數都會比為交換前來的更高分。
    In the field of cybersecurity, there are lots of classifiers (AV vendors) and each classifier will give malware samples classified results, namely naming labels to include malware families. Unfortunately, each label does not have a fixed answer based on fact like handwritten number recognition but based on each classifiers’ viewpoints, thus, we want to know which classifier receives the most support from others. Instead of using majority voting, we develop a scoring system Pairwise Consensus Score-PCS with the idea of pairwise comparison. In addition, based on the scoring system, we propose a heuristic genetic algorithm-HAGL to obtain a group of labels that unify all classifiers and get the optimized consensus score. In the research, we found that our method had a better performance than other traditional data mining methods and the score reach a higher level after value exchange.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    108356017
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108356017
    Data Type: thesis
    DOI: 10.6814/NCCU202101327
    Appears in Collections:[資訊管理學系] 學位論文

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