本文選題：計算機取證　切入點：電子證據(jù)　出處：《南京郵電大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計算機網(wǎng)絡(luò)的高速發(fā)展,從事計算機犯罪的活動逐漸增多,計算機取證技術(shù)的發(fā)展得到了越來越多的關(guān)注,為了滿足各種各樣的取證場景的需求,功能豐富、操作實用的取證工具已成為行業(yè)的必需,基于此,需要研究并設(shè)計出基于Windows環(huán)境下的隱秘信息取證系統(tǒng)。論文對此進(jìn)行研究,具有較強的理論意義和實際應(yīng)用價值。 論文將傳統(tǒng)的電子證據(jù)提取技術(shù)與數(shù)據(jù)挖掘技術(shù)相結(jié)合,提出集電子證據(jù)收集、電子證據(jù)分類和電子證據(jù)展示于一體的取證系統(tǒng)模型。 本文首先深度分析主流瀏覽器內(nèi)核機制,從計算機取證角度對瀏覽器痕跡提取技術(shù)進(jìn)行研究,提取大量有效的W.eb電子文檔信息；與此同時,還進(jìn)行了電子郵件取證的相關(guān)研究,對郵件系統(tǒng)構(gòu)成以及郵件編碼格式進(jìn)行了深入分析,從電子郵件取證角度對郵件頭信息進(jìn)行真?zhèn)涡澡b定分析。 針對電子證據(jù)的數(shù)據(jù)量巨大且雜亂無章的特性,論文研究了基于樸素貝葉斯算法的文本分類模型,并以網(wǎng)頁文本分類以及郵件分類實驗為例,設(shè)計融合分類思想的取證模型。論文通過多學(xué)習(xí)器與單學(xué)習(xí)器性能的仿真對比,融入了具備更高分類性能的集成學(xué)習(xí)分類思想,大大提高了取證精度以及取證效率,有效地將計算機取證技術(shù)與數(shù)據(jù)挖掘領(lǐng)域的分類技術(shù)進(jìn)行了融合,最終實現(xiàn)雜亂無章的海量電子證據(jù)的有效分類,提高取證效率。
[Abstract]:With the rapid development of computer network, the activities engaged in computer crime are increasing gradually, and the development of computer forensics technology has been paid more and more attention. In order to meet the needs of various forensics scenes, it has rich functions. It is necessary to study and design a secret information forensics system based on Windows, which has strong theoretical significance and practical application value. This paper combines the traditional electronic evidence extraction technology with the data mining technology, and puts forward a system model of evidence collection, electronic evidence classification and electronic evidence display. In this paper, we first deeply analyze the mainstream browser kernel mechanism, research the browser trace extraction technology from the computer forensics point of view, extract a large number of effective W. EB electronic document information; at the same time, In addition, the related research of email forensics is carried out, the composition of mail system and the mail coding format are analyzed in depth, and the authenticity of email header information is analyzed from the point of view of e-mail forensics. In view of the large amount of electronic evidence and chaotic characteristics, the text classification model based on naive Bayes algorithm is studied in this paper, and the experiments of web page text classification and mail classification are taken as examples. By comparing the performance of multiple learning devices and single learning devices, the thesis integrates the integrated learning classification idea with higher classification performance, which greatly improves the accuracy and efficiency of evidence collection. The computer forensics technology and the classification technology in the field of data mining have been effectively combined to realize the effective classification of mass electronic evidence and improve the efficiency of evidence collection.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.1;D918.2

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本文編號：1609713