基于Levenberg-Marquardt算法的主机入侵检测系统研究

Research of host intrusion detection systems based on levenberg-marquardt algorithm

入侵检测系统是当前信息安全领域的研究热点,在保障信息安全方面起着重要的作用。对BP神经网络优化算法进行对比研究的基础上,利用Levenberg-Marquardt算法对传统BP算法进行改进,成功地将LMBP算法运用到基于W indows操作系统的主机入侵检测中去,建立LMBP-HIDS入侵检测系统模型。实验结果表明,运用Levenberg-Marquardt算法优化BP神经网络进行主机入侵检测,可以较好地提高学习速率,缩短训练过程。

Intrusion Detection System （IDS） is one of the research hotspots in the field of Information Security. The Levenberg-Marquardt algorithm was taken to optimize traditional BP Neural Network, and the LMBP algorithm was successfully applied to host intrusion detection systems. Then an LMBP-HIDS intrusion detection systems model was built. The result indicated that by using Levenberg-Marquardt algorithm to optimize BP Neural Network, the BP Neural Network could work more efficiently in Intrusion Detection Systems. It could improve the training speed, shorten the training process.