本文選題：網(wǎng)絡(luò)入侵檢測 + DS證據(jù)理論　； 參考：《計(jì)算機(jī)應(yīng)用研究》2016年10期

【摘要】：為了提高檢測率,采用DS證據(jù)融合技術(shù)融合多個ELM,能夠提高整個檢測系統(tǒng)的精確性。但是傳統(tǒng)的DS技術(shù)處理沖突信息源時(shí)并不理想。因此,通過引入證據(jù)之間的沖突強(qiáng)度,將信息源劃分成可接受沖突和不可接受沖突,給出了新的證據(jù)理論(improved Dempster-Shafer,I-DS),同時(shí)針對ELM隨機(jī)產(chǎn)生隱層神經(jīng)元對算法性能造成影響的缺陷作出了改進(jìn)。通過實(shí)驗(yàn)表明,結(jié)合I-DS和改進(jìn)的ELM能夠更高速、更有效地判別入侵行為。
[Abstract]:In order to improve the detection rate, the accuracy of the whole detection system can be improved by using DS evidence fusion technology to fuse multiple ELMs. However, the traditional DS technology is not ideal in dealing with conflict information sources. Therefore, by introducing the intensity of conflict between evidence, the information source is divided into acceptable conflict and unacceptable conflict. In this paper, a new evidence theory is presented, and an improvement is made to the defect that the random generation of hidden layer neurons in ELM has an effect on the performance of the algorithm. Experiments show that the combination of I-DS and improved ELM can distinguish intrusion behavior more efficiently and efficiently.
【作者單位】： 江蘇科技大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61305058) 江蘇省自然科學(xué)基金資助項(xiàng)目(BK20130471)
【分類號】：TP393.08
，

本文編號：1981065