發(fā)布時(shí)間：2018-04-04 16:01

  本文選題：托攻擊　切入點(diǎn)：項(xiàng)目流行度　出處：《工程科學(xué)與技術(shù)》2017年01期

【摘要】：針對(duì)有監(jiān)督檢測(cè)方法在檢測(cè)托攻擊時(shí)準(zhǔn)確率不高的問題,提出一種基于項(xiàng)目流行度和新穎度分類特征的托攻擊檢測(cè)算法。首先,根據(jù)真實(shí)概貌和攻擊概貌在選擇評(píng)分項(xiàng)目方式上不同,從流行度和新穎度角度,提出有效區(qū)分正常用戶和攻擊用戶的特征;然后,基于這些特征提出一種集成檢測(cè)框架,通過Boosting提升技術(shù)產(chǎn)生多個(gè)差異較大的基分類器,并且通過融合帶有權(quán)重的基分類器的預(yù)測(cè)值得到最終的檢測(cè)結(jié)果。實(shí)驗(yàn)結(jié)果表明,基于項(xiàng)目流行度和新穎度分類特征的托攻擊檢測(cè)算法能夠提高攻擊檢測(cè)的準(zhǔn)確率和召回率。
[Abstract]:In order to solve the problem that the accuracy of supervised detection method is not high when detecting support attacks, an algorithm based on item popularity and novelty classification features is proposed.First of all, according to the differences in the selection of scoring items between the real profile and the attack profile, the features of distinguishing normal users from attacking users are proposed from the perspective of popularity and novelty, and then an integrated detection framework based on these features is proposed.The Boosting lifting technique is used to generate many different base classifiers, and the final detection results are obtained by merging the prediction values of the basis classifiers with weights.The experimental results show that the algorithm based on item popularity and novelty can improve the accuracy and recall of attack detection.
【作者單位】： 燕山大學(xué)信息科學(xué)與工程學(xué)院;河北省計(jì)算機(jī)虛擬技術(shù)與系統(tǒng)集成重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61379116) 河北省自然科學(xué)基金資助項(xiàng)目(F2015203046) 河北省高等學(xué)校科學(xué)技術(shù)研究重點(diǎn)資助項(xiàng)目(ZH2012028)
【分類號(hào)】：TP309

【參考文獻(xiàn)】

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1 李文濤;高e，

本文編號(hào)：1710660