本文選題：入侵檢測 + BP神經(jīng)網(wǎng)絡　； 參考：《解放軍信息工程大學》2014年碩士論文

【摘要】：由于網(wǎng)絡本身開放性和自由性的特點,導致一些非法分子的攻擊,惡意破壞或侵犯網(wǎng)絡,安全問題日趨突出。攻擊網(wǎng)絡的手段和技術不斷更新,使得傳統(tǒng)的防火墻、數(shù)字認證等安全防護措施已經(jīng)不能滿足網(wǎng)絡安全的需求,入侵檢測技術應運而生。然而由于入侵檢測算法的局限性,目前的入侵檢測系統(tǒng)仍然存在實時性差、誤報率高等不足。本文分析傳統(tǒng)BP神經(jīng)網(wǎng)絡構建入侵檢測模型在收斂速度和漏報率方面存在的缺陷,提出將改進的粒子群算法應用到入侵檢測系統(tǒng)中；通過研究Probing和Dos的攻擊原理,分析這Dos攻擊方法的特征,提取特征數(shù)據(jù),建立特征集合,設計一種基于改進PSO和BP神經(jīng)網(wǎng)絡的入侵檢測模型,并在此模型的基礎設計網(wǎng)絡入侵檢測系統(tǒng),通過仿真試驗證明系統(tǒng)在誤報率、收斂速度及漏報率方面的改進效果。本文所作的主要研究工作包括以下內(nèi)容：(1)分析標準粒子群算法與基本BP神經(jīng)網(wǎng)絡構建入侵檢測模型存在的不足,通過引入慣性權重因子、動態(tài)收縮因子、變異操作和多目標尋優(yōu)等策略改進粒子群算法,將和改進后的粒子群算法優(yōu)化BP神經(jīng)網(wǎng)絡。(2)利用MATLAB工具進行BP神經(jīng)網(wǎng)絡的設計,從KDDCUP的數(shù)據(jù)集中提取訓練數(shù)據(jù)和測試數(shù)據(jù),對神經(jīng)網(wǎng)絡進行訓練。(3)將訓練好的BP神經(jīng)網(wǎng)絡用于入侵檢測,構建基于優(yōu)化BP神經(jīng)網(wǎng)絡的入侵檢測系統(tǒng),為了提高系統(tǒng)的防御能力,通過系統(tǒng)與防火墻、殺毒軟件、反間諜軟件等的聯(lián)動,建立全方位的系統(tǒng)防護體系,使系統(tǒng)具有主動防御的能力。最后設計實驗環(huán)境和平臺,對基于改進PSO-BP神經(jīng)網(wǎng)絡的入侵檢測系統(tǒng)進行性能分析,驗證系統(tǒng)在檢測Probing攻擊和Dos攻擊方面的檢測能力,并將其與傳統(tǒng)的BP神經(jīng)網(wǎng)絡進行對比。實驗結果表明,基于改進PSO-BP神經(jīng)網(wǎng)絡的入侵檢測系統(tǒng)能夠有效在阻止來自網(wǎng)絡上的惡意攻擊,提高了檢測的效率和處理性能,降低了漏報率和誤報率；同時也證明了改進PSO-BP申經(jīng)網(wǎng)絡用于入侵檢測的可行性。
[Abstract]:Because of the openness and freedom of the network, some illegal elements attack, maliciously destroy or violate the network, and the security problem becomes more and more serious. The means and technology of attacking network are constantly updated, which makes traditional security measures such as firewall, digital authentication and so on can not meet the needs of network security. Intrusion detection technology emerges as the times require. However, due to the limitations of intrusion detection algorithm, the current intrusion detection system still has poor real-time performance and high false alarm rate. This paper analyzes the shortcomings of the traditional BP neural network in constructing intrusion detection model in terms of convergence speed and false report rate, and proposes to apply the improved particle swarm optimization algorithm to the intrusion detection system, and studies the attack principle of probe and Dos. This paper analyzes the features of the Dos attack method, extracts the feature data, establishes the feature set, designs an intrusion detection model based on improved PSO and BP neural network, and designs a network intrusion detection system based on this model. Simulation results show that the system can improve the false alarm rate, convergence rate and false alarm rate. The main research work in this paper includes the following contents: 1) analyzing the shortcomings of standard particle swarm optimization algorithm and basic BP neural network in constructing intrusion detection model. By introducing inertia weight factor and dynamic shrinkage factor, Mutation operation and multi-objective optimization are used to improve particle swarm optimization. The improved particle swarm optimization algorithm is used to optimize BP neural network. MATLAB is used to design BP neural network. The training data and test data are extracted from the data set of KDDCUP. The BP neural network is used in intrusion detection, and an intrusion detection system based on optimized BP neural network is constructed. In order to improve the defense ability of the system, antivirus software is used through the system and firewall. The linkage of anti-spyware software, the establishment of an all-round system protection system, so that the system has the ability of active defense. Finally, the experimental environment and platform are designed to analyze the performance of intrusion detection system based on improved PSO-BP neural network, and verify the detection ability of the system in detecting probe attack and dos attack, and compare it with the traditional BP neural network. The experimental results show that the intrusion detection system based on improved PSO-BP neural network can effectively prevent malicious attacks from the network, improve the detection efficiency and processing performance, and reduce the false alarm rate and false alarm rate. At the same time, it also proves the feasibility of improving PSO-BP network for intrusion detection.
【學位授予單位】：解放軍信息工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.08;TP183

【參考文獻】

相關期刊論文 前10條

1 張寶X;張寶一;;基于BP神經(jīng)網(wǎng)絡的非線性函數(shù)擬合[J];電腦知識與技術;2012年27期

2 井小沛;汪厚祥;聶凱;羅志偉;;面向入侵檢測的基于IMGA和MKSVM的特征選擇算法[J];計算機科學;2012年07期

3 牟琦;畢孝儒;厙向陽;;基于GQPSO算法的網(wǎng)絡入侵特征選擇方法[J];計算機工程;2011年14期

4 吳曉軍;楊戰(zhàn)中;趙明;;均勻搜索粒子群算法[J];電子學報;2011年06期

5 馮雪;裴志松;;粒子群優(yōu)化算法的研究與應用[J];吉林建筑工程學院學報;2011年03期

6 趙宏;;基于GA的BP神經(jīng)網(wǎng)絡在本安參數(shù)評定中的應用[J];自動化儀表;2011年04期

7 張濤;;校園網(wǎng)中入侵檢測系統(tǒng)的實驗研究[J];電腦編程技巧與維護;2011年04期

8 陳仕濤;陳國龍;郭文忠;劉延華;;基于粒子群優(yōu)化和鄰域約簡的入侵檢測日志數(shù)據(jù)特征選擇[J];計算機研究與發(fā)展;2010年07期

9 何紹榮;梁金明;何志勇;;基于互信息和關系積理論的特征選擇方法[J];計算機工程;2010年13期

10 張紅梅;范明鈺;;模式匹配BM算法改進[J];計算機應用研究;2009年09期

，

本文編號：2039635