计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
9期
81-84
,共4页
网络安全态势%支持向量机%K 近邻算法%指标体系
網絡安全態勢%支持嚮量機%K 近鄰算法%指標體繫
망락안전태세%지지향량궤%K 근린산법%지표체계
network security situation%Support Vector Machine(SVM)%K Nearest Neighbor(KNN)algorithm%index system
为了提高网络安全态势评估性能,提出一种 K 近邻和支持向量机相融合的网络安全态势评估模型(KNN-SVM).将网络安全数据集输入到支持向量机学习,找到支持向量集,对于待评估网络安全态势样本,计算其与最优分类超平面间的距离,如果距离大于阈值,采用支持向量机进行网络安全态势评估,否则采用 K 近邻进行评估,以解决支持向量机对超平面附近样本易错分的缺陷,减少 SVM 的误判率.仿真结果表明,相对于单独 SVM,KNN-SVM 提高了网络安全态势评估正确率,而且性能更加稳定.
為瞭提高網絡安全態勢評估性能,提齣一種 K 近鄰和支持嚮量機相融閤的網絡安全態勢評估模型(KNN-SVM).將網絡安全數據集輸入到支持嚮量機學習,找到支持嚮量集,對于待評估網絡安全態勢樣本,計算其與最優分類超平麵間的距離,如果距離大于閾值,採用支持嚮量機進行網絡安全態勢評估,否則採用 K 近鄰進行評估,以解決支持嚮量機對超平麵附近樣本易錯分的缺陷,減少 SVM 的誤判率.倣真結果錶明,相對于單獨 SVM,KNN-SVM 提高瞭網絡安全態勢評估正確率,而且性能更加穩定.
위료제고망락안전태세평고성능,제출일충 K 근린화지지향량궤상융합적망락안전태세평고모형(KNN-SVM).장망락안전수거집수입도지지향량궤학습,조도지지향량집,대우대평고망락안전태세양본,계산기여최우분류초평면간적거리,여과거리대우역치,채용지지향량궤진행망락안전태세평고,부칙채용 K 근린진행평고,이해결지지향량궤대초평면부근양본역착분적결함,감소 SVM 적오판솔.방진결과표명,상대우단독 SVM,KNN-SVM 제고료망락안전태세평고정학솔,이차성능경가은정.
In order to improve the network security situation assessment performance, this paper proposes assessment model (KNN-SVM)which integrates the K Nearest Neighbor with Support Vector Machine. The network security data set is input to the Support Vector Machine to learn and finds support vector set. When the distance between the sample of network security situ-ation and the optimal classification hyper plane is bigger than threshold, the Support Vector Machines are used to assess the net-work security situation, otherwise the K Nearest Neighbor is used to assess the network security situation to solve the defects and reduce the error rate of SVM. The simulation results show that, compared with the single SVM, KNN-SVM improves net-work security situation assessment accuracy and has more stable performance.
