本文選題：入侵檢測 + 粗糙集理論��； 參考：《江蘇科技大學》2015年碩士論文

【摘要】：伴隨著互聯(lián)網(wǎng)絡和計算機技術的高速發(fā)展,互聯(lián)網(wǎng)通信已經(jīng)滲透到政治、經(jīng)濟、文化和生活以及科學的各個領域,極大地影響著人類社會各個方面的進步和發(fā)展。同時,它也極大地影響和改變著人們的生活、工作和學習。但是,隨著互聯(lián)網(wǎng)絡的發(fā)展,各種安全問題也隨之出現(xiàn)。有關統(tǒng)計數(shù)據(jù)顯示,在全球范圍內(nèi),每隔20秒就會發(fā)生一起網(wǎng)絡入侵事件。網(wǎng)絡上的黑客能夠輕松的盜取你的私密文件,盜取你的銀行存款信息,破壞你的個人賬目信息,將你的私密信函公之于眾,隨意改正、擾亂和破壞你的數(shù)據(jù)庫中的信息,甚至直接破壞你的磁盤和計算機等硬件設施,導致你的網(wǎng)絡處于癱瘓或者崩潰狀態(tài)。所以,研究一些切實有效的互聯(lián)網(wǎng)安全技術來保障計算機系統(tǒng)和互聯(lián)網(wǎng)系統(tǒng)的安全,已經(jīng)成為學術界和商界研究的熱點問題。入侵檢測是一種主動防御入侵的安全方式,是維護網(wǎng)絡安全的主要模塊。因此該技術成為人們研究的熱點。目前入侵檢測技術正在朝著自動化和智能化方向發(fā)展。所謂入侵檢測的智能化是將遺傳算法和人工神經(jīng)網(wǎng)絡算法等智能化算法用于入侵檢測。傳統(tǒng)的入侵檢測系統(tǒng)誤報率和漏報率都很高,不能夠很好的識別新興的攻擊類型。隨著網(wǎng)絡數(shù)據(jù)量的急劇增加,傳統(tǒng)的入侵檢測技術不能勝任實時性的要求。此外,傳統(tǒng)的遺傳算法和神經(jīng)網(wǎng)絡算法還具有收斂速度慢和容易陷入局部極小值等缺點。本文通過深入分析研究入侵檢測的特點和遺傳算法以及神經(jīng)網(wǎng)絡算法的結構。為了適應實時性要求,首先基于粗糙集理論,對傳統(tǒng)遺傳算法的選擇交叉變異等環(huán)節(jié)做了一系列改進。利用了粗糙集理論的知識,重新設置了適應度函數(shù),使其能夠快速刪除冗余的屬性,約簡大型知識系統(tǒng)。然后對神經(jīng)網(wǎng)絡算法添加自己的想法,做了一系列改進,將約簡后的大型知識系統(tǒng)輸入到改進后神經(jīng)網(wǎng)絡中,使其能夠正確高效的識別入侵數(shù)據(jù)。最后在Windows環(huán)境下利用Rosetta和MATLAB軟件編寫仿真程序,采用KDDCUP99數(shù)據(jù)集,設置了兩組試驗以證明本文提出的算法的有效性。
[Abstract]:With the rapid development of Internet and computer technology, Internet communication has penetrated into the fields of politics, economy, culture, life and science, which has greatly affected the progress and development of all aspects of human society. At the same time, it also greatly affects and changes people's life, work and study. However, with the development of Internet, all kinds of security problems appear. Statistics show that globally, a cyber intrusion occurs every 20 seconds. Hackers on the Internet can easily steal your private documents, steal your bank deposit information, destroy your personal accounts, publish your private letters to the public, correct them at will, disrupt and destroy the information in your database. Even directly damage your disk and computer hardware facilities, resulting in your network in a state of paralysis or crash. Therefore, the research of some effective Internet security technologies to protect the security of computer systems and Internet systems has become a hot issue in academia and business circles. Intrusion detection is a kind of active defense against intrusion, and it is the main module to maintain network security. Therefore, this technology has become the focus of research. At present, intrusion detection technology is developing towards automation and intelligence. Intelligent intrusion detection is based on genetic algorithm (GA) and artificial neural network (Ann). The traditional intrusion detection system has high false alarm rate and false alarm rate, so it can not identify the emerging attack types. With the rapid increase of network data, the traditional intrusion detection technology can not meet the requirements of real-time. In addition, the traditional genetic algorithm and neural network algorithm also have the disadvantages of slow convergence speed and easy to fall into local minimum. In this paper, the characteristics of intrusion detection and the structure of genetic algorithm and neural network algorithm are studied. In order to meet the real-time requirements, a series of improvements are made to the selection of crossover mutation in traditional genetic algorithm based on rough set theory. By using the knowledge of rough set theory, the fitness function is set up so that the redundant attributes can be quickly deleted and the large knowledge system can be reduced. Then we add our own ideas to the neural network algorithm and make a series of improvements to input the reduced large-scale knowledge system into the improved neural network so that it can recognize intrusion data correctly and efficiently. At last, the simulation program is compiled by Rosetta and MATLAB in Windows environment, and the KDDCUP99 data set is used to set up two groups of experiments to prove the validity of the proposed algorithm.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18;TP393.08

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本文編號：2050361