基于混合差分演化的网络入侵检测算法
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摘要
基于机器学习方法的入侵检测算法是目前网络设备检测领域的研究热点.网络入侵检测源数据的多样性是影响机器学习方法在该领域实际应用性能的主要因素.研究通过设计多扰动向量混合差分演化算法,稳定地优化了最小二乘支持向量机模型的关键参数;在不增加测试集检测计算复杂性的前提下,通过最优化参数的方式,提高了最小二乘支持向量机算法入侵检测的精度和稳定性.KDD Cup 99测试集的仿真实验结果显示,所提出的基于混合差分演化的网络入侵检测算法比目前多种同类算法有着更好的平均性能.
Intrusion detection algorithm based on machine learning method is one of research hotspot in the field of network equipment testing. In the face of the real-world application requirement,machine learning methods should be further optimized to achieve accurate and stable detection effect. The study optimize steadily several key parameters of least squares support vector machine(SVM) by designing a hybrid differential evolution algorithm with disturbance vector and improved the intrusion detection accuracy and stability of least squares support vector machine(SVM) algorithm by means of adaptive parameter tuning. The experimental results in KDD Cup 09 test set showed that,the proposed network intrusion detection algorithm based on hybrid differential evolution algorithm had better performance on average than many similar algorithm at present.
Intrusion detection algorithm based on machine learning method is one of research hotspot in the field of network equipment testing. In the face of the real-world application requirement,machine learning methods should be further optimized to achieve accurate and stable detection effect. The study optimize steadily several key parameters of least squares support vector machine(SVM) by designing a hybrid differential evolution algorithm with disturbance vector and improved the intrusion detection accuracy and stability of least squares support vector machine(SVM) algorithm by means of adaptive parameter tuning. The experimental results in KDD Cup 09 test set showed that,the proposed network intrusion detection algorithm based on hybrid differential evolution algorithm had better performance on average than many similar algorithm at present.
引文
[1]HU W M,GAO J,WANG Y G,et al.Online adaboostbased parameterized methods for dynamic distributed network intrusion detection[J].IEEE transactions on cybernetics,2014,44(1):66-82.
[2]刘羿.蝙蝠算法优化神经网络的网络入侵检测[J].计算机仿真,2015,32(2):311-314.
[3]李振刚,甘泉.改进蚁群算法优化SVM参数的网络入侵检测模型研究[J].重庆邮电大学学报(自然科学版),2014,26(6):785-789.
[4]王亚,熊焰,龚旭东,等.基于混沌PSO算法优化RBF网络入侵检测模型[J].计算机工程与应用,2013,49(10):84-87.
[5]DASTANPOUR A,IBRAHIM S,MASHINCHI R,et al.Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system[J].Open systems,2014,77(10):72-77.
[6]马琰,闫兵.基于混沌差分优化算法的网络入侵检测系统[J].科学技术与工程,2013,13(36):10967-10970.
[7]边根庆,赵宏,张维琪,等.基于免疫克隆与差分进化的入侵检测方法[J].微电子学与计算机,2012,29(5):124-128.
[8]SAILAGA M,KUMAR R K,MURTY P S R.Intrusion detection model based on differential evolution[J].International journal of computer applications,2011,36(6):10-13.
[9]STORN R,PRICE K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of global optimization,1997,11(4):341-359.
[10]BREST J,UMER V,MAUCEC M.Self-adaptive differential evolution algorithm in constrained real-parameter optimization[J].IEEE congress on evolutionary computation,2006,98:215-222.
[11]DENG W,YANG X,ZOU L,et al.An improved self-adaptive differential evolution algorithm and application[J].Chemometrics and intelligent laboratory systems,2013,128(15):66-76.
[12]YU C C,CHEN J,HUANG Q,et al.A new hybrid differential evolution algorithm with simulated annealing and adaptive Gaussian immune[C]∥2012 8th IEEE International Conference on Natural Computation(ICNC).Chongqin,China:IEEE,2012:600-607.
[2]刘羿.蝙蝠算法优化神经网络的网络入侵检测[J].计算机仿真,2015,32(2):311-314.
[3]李振刚,甘泉.改进蚁群算法优化SVM参数的网络入侵检测模型研究[J].重庆邮电大学学报(自然科学版),2014,26(6):785-789.
[4]王亚,熊焰,龚旭东,等.基于混沌PSO算法优化RBF网络入侵检测模型[J].计算机工程与应用,2013,49(10):84-87.
[5]DASTANPOUR A,IBRAHIM S,MASHINCHI R,et al.Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system[J].Open systems,2014,77(10):72-77.
[6]马琰,闫兵.基于混沌差分优化算法的网络入侵检测系统[J].科学技术与工程,2013,13(36):10967-10970.
[7]边根庆,赵宏,张维琪,等.基于免疫克隆与差分进化的入侵检测方法[J].微电子学与计算机,2012,29(5):124-128.
[8]SAILAGA M,KUMAR R K,MURTY P S R.Intrusion detection model based on differential evolution[J].International journal of computer applications,2011,36(6):10-13.
[9]STORN R,PRICE K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of global optimization,1997,11(4):341-359.
[10]BREST J,UMER V,MAUCEC M.Self-adaptive differential evolution algorithm in constrained real-parameter optimization[J].IEEE congress on evolutionary computation,2006,98:215-222.
[11]DENG W,YANG X,ZOU L,et al.An improved self-adaptive differential evolution algorithm and application[J].Chemometrics and intelligent laboratory systems,2013,128(15):66-76.
[12]YU C C,CHEN J,HUANG Q,et al.A new hybrid differential evolution algorithm with simulated annealing and adaptive Gaussian immune[C]∥2012 8th IEEE International Conference on Natural Computation(ICNC).Chongqin,China:IEEE,2012:600-607.