基于人工蜂群算法的计算机网络DDoS攻击检测方法

Computer Network DDoS Attack Detection Method Based on Artificial Bee Colony Algorithm

摘要:

计算机网络在DDoS入侵下容易出现停止服务、网络崩溃,为了提高网络安全性,提出基于人工蜂群算法的计算机网络DDoS攻击检测方法。根据特征样本之间的相关性构建计算机网络DDoS攻击的自适应的入侵检测信息分析模型,根据网络数据流与潜在空间之间的映射关系,结合测试样本和学习样本之间特征差异性进行DDoS攻击数据特征提取,在基站上设置入侵检测数据处理终端,采用人工蜂群算法实现对计算机网络攻击检测的个体最优值和全局最优值寻优,根据人工蜂群的动态寻优和组合优化结果,实现对组合网络流量数据间的攻击信息特征提取和聚类分析,解决计算机网络DDoS攻击检测过程中的连续多变量优化问题。仿真测试结果表明,采用该方法进行计算机网络DDoS攻击检测的寻优能力较好,精度和效率高于传统方法。

Abstract:

The computer network is prone to stop service and network crash under the intrusion of DDoS. In order to improve the network security, a DDoS attack detection method of computer network based on artificial bee colony algorithm is proposed. According to the correlation between feature samples, an adaptive intrusion detection information analysis model of computer network DDoS attack is constructed. According to the mapping relationship between network data stream and potential space, the features of DDoS attack data are extracted by combining the feature differences between test samples and learning samples. An intrusion detection data processing terminal is set up on the base station, and the artificial bee colony algorithm is used to optimize the individual optimal value and the global optimal value of computer network attack detection. According to the results of dynamic optimization and combinatorial optimization of artificial bee colony, the feature extraction and clustering analysis of attack information between combined network traffic data are realized, and the continuous multivariable optimization problem in the process of DDoS attack detection in computer network is solved. The simulation test results show that this method has better optimization ability, higher accuracy and efficiency than the traditional method.