【摘要】：BP算法是入侵檢測(cè)系統(tǒng)(IDS)的一種重要檢測(cè)方法,但是該算法存在檢測(cè)時(shí)間長(zhǎng)、效率低下等局限性.為了解決以上問(wèn)題,通過(guò)對(duì)BP算法的深入剖析,提出了一種面向入侵檢測(cè)系統(tǒng)的新算法——EBP(Enhanced BP)算法.EBP算法通過(guò)改進(jìn)誤差函數(shù)、增加可自適應(yīng)放大的誤差信號(hào)等方法,較好地解決了IDS的檢測(cè)速度慢、檢測(cè)效率低下等問(wèn)題.實(shí)驗(yàn)結(jié)果表明,EBP算法相比經(jīng)典的BP算法和MBP算法,不僅在網(wǎng)絡(luò)訓(xùn)練的收斂速度上有了明顯改善,而且在IDS的檢測(cè)效率上分別提高了14.0%和8.1%,在誤報(bào)率上分別降低了4.45%和1.5%.
[Abstract]:BP algorithm is an important detection method for intrusion detection system (IDS), but it has some limitations such as long detection time and low efficiency. In order to solve the above problems, through the thorough analysis of BP algorithm, a new algorithm for intrusion detection system-EBP (Enhanced BP) algorithm is proposed. The EBP algorithm can increase the self-adaptive amplification error signal by improving the error function, and so on. The problems of low detection speed and low detection efficiency of IDS are well solved. The experimental results show that compared with the classical BP algorithm and MBP algorithm, the EBP algorithm not only improves the convergence speed of network training obviously, but also improves the detection efficiency of IDS by 14.0% and 8.1%, respectively. The false positive rate decreased by 4.45% and 1.5% respectively.
【作者單位】： 內(nèi)蒙古科技大學(xué)包頭師范學(xué)院計(jì)算機(jī)系;內(nèi)蒙古科技大學(xué)信息工程學(xué)院計(jì)算機(jī)系;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61163025) 內(nèi)蒙古自治區(qū)自然科學(xué)基金項(xiàng)目(2010BS0904) 內(nèi)蒙古自治區(qū)高等學(xué)�？茖W(xué)研究基金項(xiàng)目(NJ10162,NJZY14242,NJZY201) 包頭市科學(xué)研究基金項(xiàng)目(2014S2004-3-1-26)
【分類號(hào)】：TP393.08

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 周國(guó)雄;沈?qū)W杰;李琳;賀超英;;基于AdaBoost的網(wǎng)絡(luò)入侵智能檢測(cè)[J];系統(tǒng)仿真學(xué)報(bào);2014年07期

2 楊雅輝;黃海珍;沈晴霓;吳中海;張英;;基于增量式GHSOM神經(jīng)網(wǎng)絡(luò)模型的入侵檢測(cè)研究[J];計(jì)算機(jī)學(xué)報(bào);2014年05期

3 程s，

本文編號(hào)：2318906