本文選題：網絡入侵檢測 + DS證據理論　； 參考：《計算機應用研究》2016年10期

【摘要】：為了提高檢測率,采用DS證據融合技術融合多個ELM,能夠提高整個檢測系統(tǒng)的精確性。但是傳統(tǒng)的DS技術處理沖突信息源時并不理想。因此,通過引入證據之間的沖突強度,將信息源劃分成可接受沖突和不可接受沖突,給出了新的證據理論(improved Dempster-Shafer,I-DS),同時針對ELM隨機產生隱層神經元對算法性能造成影響的缺陷作出了改進。通過實驗表明,結合I-DS和改進的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】： 江蘇科技大學計算機科學與工程學院;
【基金】：國家自然科學基金資助項目(61305058) 江蘇省自然科學基金資助項目(BK20130471)
【分類號】：TP393.08
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本文編號：1981065