计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
113-116,155
,共5页
协同量子粒子群算法%最小二乘支持向量机%特征选择%网络入侵检测
協同量子粒子群算法%最小二乘支持嚮量機%特徵選擇%網絡入侵檢測
협동양자입자군산법%최소이승지지향량궤%특정선택%망락입침검측
cooperative quantum-behaved particle swarm optimization algorithm%least square support vector machine%feature selection%network intrusion detection
为了提高网络入侵检测率，提出一种协同量子粒子群算法和最小二乘支持向量机的网络入侵检测模型（CQPSO-LSSVM）。将网络特征子集编码成量子粒子位置，入侵检测正确率作为特征子集优劣的评价标准，采用协同量子粒子群算法找到最优特征子集，采用最小二乘支持向量机建立网络入侵检测模型，并采用KDD CUP 99数据集进行仿真测试。结果表明，CQPSO-LSSVM获得了比其他入侵检测模型更高的检测效率和检测率。
為瞭提高網絡入侵檢測率，提齣一種協同量子粒子群算法和最小二乘支持嚮量機的網絡入侵檢測模型（CQPSO-LSSVM）。將網絡特徵子集編碼成量子粒子位置，入侵檢測正確率作為特徵子集優劣的評價標準，採用協同量子粒子群算法找到最優特徵子集，採用最小二乘支持嚮量機建立網絡入侵檢測模型，併採用KDD CUP 99數據集進行倣真測試。結果錶明，CQPSO-LSSVM穫得瞭比其他入侵檢測模型更高的檢測效率和檢測率。
위료제고망락입침검측솔，제출일충협동양자입자군산법화최소이승지지향량궤적망락입침검측모형（CQPSO-LSSVM）。장망락특정자집편마성양자입자위치，입침검측정학솔작위특정자집우렬적평개표준，채용협동양자입자군산법조도최우특정자집，채용최소이승지지향량궤건립망락입침검측모형，병채용KDD CUP 99수거집진행방진측시。결과표명，CQPSO-LSSVM획득료비기타입침검측모형경고적검측효솔화검측솔。
In order to improve the detection rate of network intrusion, a novel network intrusion detection model is pro-posed in this paper based on cooperative quantum-behaved particle swarm optimization algorithm and least square sup-port vector machine. The feature subset is coded as the position of particle, and the detection rate is taken as evaluation cri-teria of the feature subset, and the cooperative quantum-behaved particle swarm optimization algorithm is used to find the optimal feature subset, the intrusion detection model is built based on the optimal feature subset by least square support vector machine, the simulation experiment is carried out on the KDD CUP 99 data. The results show that, compared with other models, the proposed algorithm has improved detection efficiency and the detection rate of the network intrusion.
