本文選題：粗糙集 + 劃分　； 參考：《遼寧科技大學(xué)》2014年碩士論文

【摘要】：隨著信息技術(shù)的不斷成熟和網(wǎng)絡(luò)技術(shù)的飛速發(fā)展，，網(wǎng)絡(luò)逐漸成為人們生活中不可缺少的一部分。但是，人們在享受著信息革命所帶來的便利的同時，也將不可避免的面對信息網(wǎng)絡(luò)安全問題所帶來的巨大挑戰(zhàn)。作為網(wǎng)絡(luò)安全防護(hù)機(jī)制的關(guān)鍵環(huán)節(jié)和網(wǎng)絡(luò)安全技術(shù)的一大核心技術(shù)，入侵檢測技術(shù)正得到快速的發(fā)展并日漸成熟起來。 基于數(shù)據(jù)挖掘的入侵檢測，將整個入侵檢測系統(tǒng)建立的過程視為一個對訓(xùn)練數(shù)據(jù)集的挖掘過程。最大限度地降低了對領(lǐng)域先驗知識的需求和人工的參與程度，明顯地提高了入侵檢測和入侵響應(yīng)的效率。 粗糙集理論作為一種擁有著成熟數(shù)學(xué)基礎(chǔ)且不需要先驗知識支持的用于分析和處理不確定、不完整、不一致性信息的有效工具，無論是對數(shù)據(jù)挖掘的預(yù)處理階段還是在數(shù)據(jù)挖掘階段都能起到很大的幫助作用。 本文在對kddcup99數(shù)據(jù)集進(jìn)行一定的統(tǒng)計分析研究的基礎(chǔ)之上，運用粗糙集理論中的等價類劃分思想。首先利用一定的先驗知識根據(jù)service的取值不同對原kddcup99訓(xùn)練數(shù)據(jù)集進(jìn)行等價類劃分，解決了現(xiàn)有研究中kddcup99數(shù)據(jù)集因龐大而不易處理的難題。然后根據(jù)傳統(tǒng)離散化算法對各個劃分進(jìn)行離散化操作，極大地減小了后期運算的復(fù)雜程度。其次在屬性約簡和值約簡過程中，再次將劃分思想運用其中，將二者合二為一，在保證整個決策表一致性的前提下，得到了近似最小的約簡結(jié)果，最后以此為依據(jù)，快速準(zhǔn)確地建立起整個入侵檢測系統(tǒng)規(guī)則庫。實驗結(jié)果表明，本文方法在保證高檢測率低誤報率低漏檢率的前提下，明顯地降低了數(shù)據(jù)挖掘過程各階段的的復(fù)雜程度。
[Abstract]:With the continuous maturity of information technology and the rapid development of network technology, the network has gradually become an indispensable part of people's life. However, while enjoying the convenience brought by the information revolution, people will inevitably face the enormous challenge brought by the information network security. As a key link of network security protection mechanism and a core technology of network security technology, intrusion detection technology is developing rapidly and maturing day by day. Intrusion detection based on data mining, the process of establishing the whole intrusion detection system is regarded as a mining process of the training data set. The requirement of domain prior knowledge and the degree of human participation are greatly reduced, and the efficiency of intrusion detection and intrusion response is obviously improved. Rough set theory is an effective tool for analyzing and processing uncertain, incomplete and inconsistent information, which has a mature mathematical foundation and does not require prior knowledge support. Both the preprocessing stage and the data mining stage of data mining can be of great help. Based on the statistical analysis of kddcup99 data sets, this paper applies the theory of equivalent class partition in rough set theory. Firstly, the prior knowledge is used to partition the original kddcup99 training data set according to the value of service, which solves the problem that the kddcup99 data set is difficult to deal with because of its huge size. According to the traditional discretization algorithm, each partition is discretized, which greatly reduces the complexity of the later operation. Secondly, in the process of attribute reduction and value reduction, the partition idea is used again, and the two are combined into one. On the premise of ensuring the consistency of the whole decision table, the approximate minimum reduction results are obtained. The rule base of the whole intrusion detection system is established quickly and accurately. The experimental results show that the method can obviously reduce the complexity of the data mining process on the premise of high detection rate and low false alarm rate.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.08

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 吳靜;劉衍珩;孟凡雪;;入侵檢測中的多分類SVM增量學(xué)習(xí)算法[J];北京工業(yè)大學(xué)學(xué)報;2009年12期

2 楊宏宇;朱丹;謝豐;謝麗霞;;入侵異常檢測研究綜述[J];電子科技大學(xué)學(xué)報;2009年05期

3 姚玉獻(xiàn);;網(wǎng)絡(luò)安全與入侵檢測[J];計算機(jī)安全;2007年05期

4 鐘將;馮永;李志國;葉春曉;;基于自適應(yīng)免疫分類器的入侵檢測[J];重慶大學(xué)學(xué)報(自然科學(xué)版);2007年07期

5 張國權(quán);李文立;;基于混合互信息的決策樹入侵檢測[J];遼寧工程技術(shù)大學(xué)學(xué)報(自然科學(xué)版);2009年02期

6 張清華;幸禹可;;一種基于Hash的快速值約簡方法[J];廣西師范大學(xué)學(xué)報(自然科學(xué)版);2011年04期

7 楊智君;田地;馬駿驍;隋欣;周斌;;入侵檢測技術(shù)研究綜述[J];計算機(jī)工程與設(shè)計;2006年12期

8 柳景超;耿伯英;宋勝鋒;;入侵檢測中加權(quán)頻繁項集挖掘[J];計算機(jī)工程與設(shè)計;2008年08期

9 章金熔;劉峰;趙志宏;駱斌;;數(shù)據(jù)挖掘方法在網(wǎng)絡(luò)入侵檢測中的應(yīng)用[J];計算機(jī)工程與設(shè)計;2009年24期

10 卿斯?jié)h ,蔣建春 ,馬恒太 ,文偉平 ,劉雪飛;入侵檢測技術(shù)研究綜述[J];通信學(xué)報;2004年07期

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