基于优化粒子群算法的网络入侵小信号检测模型

Network Intrusion Small Signal Detection Model Based on Optimization Particle Swarm Algorithm

随着计算机技术的飞速发展与计算机网络的广泛应用,网络的安全性逐步成为人们关注的焦点。现阶段的网络入侵检测方法难以识别含有入侵特征小信号的网络入侵检测,检测方法自适性能力差,导致网络入侵检测漏警误警率高。为了提高网络安全,提出基于优化粒子群算法的网络入侵小信号检测模型。通过粒子间的相互协作优化聚类含有入侵特征的信号数据,通过极化阵列计算定位分离小信号,建立小信号过滤模型找寻提取带有入侵特征的小信号。仿真实验表明,优化粒子群算法的网络入侵小信号检测模型,提高了网络入侵检测的自适用性,在网络入侵信号受环境因素干扰的情况下,能够准确的检测出带有入侵特征小信号的网络入侵行为。有效的提高了网络检测的正确率,加快了网络入侵的检测速度。

With the rapid development of computer technology and the wide application of computer network. The security of the network gradually become the focus of attention. 's current network intrusion detection methods are hard to identify with invasion of small signal characteristics of the network intrusion detection, test method of adaptive sexual ability is poor, lead to network intrusion detection leak p high false alarm rate. In order to improve the network security, based on the optimization of particle swarm algorithm of network intrusion small signal detection model. Clustering are optimized by the interaction between the particles contain invasion characteristics of signal data, through the calculation of polarization array positioning separation of small signal, small signal filtering model is established for extracting small signal with invasion characteristics. Simulation experiments show that the optimization of particle swarm algorithm of network intrusion small signal detection model, improves the applicability of network intrusion detection, the network intrusion signal interference by environmental factors, accurately detect the network intrusion behavior with invasion characteristics of small signal. Ef-fectively improve the network detection accuracy, accelerated the speed of network intrusion detection.