摘要
随着网络技术的迅速发展,网络信息安全成为一个极具挑战性的研究领域。入侵检测系统(IDS)作为网络防御的一个重要角色,它对网络中流量进行实时监视,以识别各种网络安全漏洞。本文提出了基于对称不确定性特征提取和遗传算法优化参数组合的支持向量机(SU-GA-SVM)模型,并将其应用于KDDCUP’99数据集进行入侵检测仿真实验,实验结果表明该分类器能够有效地提高IDS的分类检测精度,误警率也明显降低。
With the rapid development of network technology, the security of network information has become a very challenging research field. As an important role of network defense, IDS monitors the traffic in the network in real time in order to identify various intrusions. This paper presents SU-GA-SVM model and uses it in KDDCUP＇99 data set for intrusion detection simulation experiments. The experimental results show that the accuracy of the classifier can be effectively improved, and the false alarm rate is significantly reduced.
作者
张宝华
赵莹
ZHANG Bao-hua;ZHAO Ying(Network Center,The 2nd Hospital of Tianjin Medical University,Tianjin 300211,China;Tianjin Xianshuigu No.4 Middle School,Tianjin 300350,China)
出处
《价值工程》
2018年第19期227-230,共4页
Value Engineering
