本文選題：S-Kohonen網(wǎng)絡(luò) + DoS　； 參考：《小型微型計(jì)算機(jī)系統(tǒng)》2017年03期

【摘要】：拒絕服務(wù)(Denial of Service,DoS)是企圖使其預(yù)期用戶(hù)的一臺(tái)主機(jī)或其他網(wǎng)絡(luò)資源不可用,如臨時(shí)或無(wú)限期地中斷或暫停連接到因特網(wǎng)主機(jī)的服務(wù).為了有效地阻止DoS攻擊,首先需要提高DoS攻擊檢測(cè)的準(zhǔn)確性,提出一種基于改進(jìn)Kohonen網(wǎng)絡(luò)的DoS攻擊檢測(cè)算法.該方法通過(guò)對(duì)DoS攻擊原始數(shù)據(jù)的預(yù)處理,為后續(xù)數(shù)據(jù)處理的方便和保證程序運(yùn)行時(shí)加快收斂奠定必要的基礎(chǔ),采用檢測(cè)結(jié)果的正確率作為該算法的評(píng)價(jià)指標(biāo),采用SOM學(xué)習(xí)算法是把高維空間的輸入數(shù)據(jù)映射到低維神經(jīng)網(wǎng)絡(luò)上,并且保持原來(lái)的拓?fù)浯涡?然后建立S-Kohonen(Supervised-Kohonen)神經(jīng)網(wǎng)絡(luò)檢測(cè)模型.實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)的Kohonen方法相比,S-Kohonen網(wǎng)絡(luò)具有更好的檢測(cè)性能.
[Abstract]:Denial of Service (dos) is an attempt to disable a host or other network resource of its intended user, such as temporarily or indefinitely interrupting or suspending services connected to an Internet host. In order to effectively prevent DoS attacks, it is necessary to improve the accuracy of DoS attack detection. A DoS attack detection algorithm based on improved Kohonen network is proposed. By preprocessing the raw data of DoS attack, the method lays a necessary foundation for the convenience of the subsequent data processing and the guarantee of program convergence. The correct rate of the detection result is used as the evaluation index of the algorithm. The SOM learning algorithm is used to map the input data of the high-dimensional space to the low-dimensional neural network and maintain the original topological order. Then the S-Kohonenn Supervised-Kohonen neural network detection model is established. The experimental results show that the S-Kohonen network has better detection performance than the traditional Kohonen method.
【作者單位】： 河北大學(xué)電子信息工程學(xué)院;
【基金】：國(guó)家科技支撐計(jì)劃項(xiàng)目(2013BAK07B04)資助 國(guó)家自然基金項(xiàng)目(61672205)資助 河北省自然科學(xué)基金項(xiàng)目(F2013201170)資助 河北省高等學(xué)校科學(xué)技術(shù)研究重點(diǎn)項(xiàng)目(ZD2014008)資助
【分類(lèi)號(hào)】：TP393.08

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