激光与光电子学进展, 2021, 58 (6): 0610006, 网络出版: 2021-03-01
基于乌鸦搜索优化BP神经网络的入侵检测方法 下载: 532次
Intrusion Detection Method of BP Neural Network Based on Crow Search Algorithm
图像处理 入侵检测 反向传播神经网络 乌鸦搜索算法 参数优化 image processing intrusion detection back propagation neural network crow search algorithm parameter optimization
摘要
为了提高入侵检测系统的准确率,提出一种基于乌鸦搜索算法的反向传播(CSA-BP)神经网络模型。BP神经网络是解决非线性问题的重要方法,但是其预测能力容易受到初始参数的影响。针对这一问题,将相对百分误差作为模型的目标函数,通过乌鸦搜索算法极强的全局搜索能力找到最优权值和阈值。然后,利用5组标准的数据集对CSA-BP模型进行验证。最后,将CSA-BP算法用于入侵检测系统,结果表明,该算法使入侵检测系统准确率更高,达到了96.6%,且加快了收敛速度。
Abstract
In order to improve the accuracy of the intrusion detection system, a back propagation neural network model based on the crow search algorithm (CSA-BP) is proposed. BP neural network is an important method to solve nonlinear problems, but its predictive ability is easily affected by the initial parameters. To solve this problem, the relative percentage error is used as the objective function of the model, and the optimal weight and threshold are found through the strong global search ability of the crow search algorithm. Then, the CSA-BP model is validated with five standard datasets. Finally, the CSA-BP algorithm is used in the intrusion detection system. The results show that the proposed algorithm makes the intrusion detection system more accurate, reaching 96.6%, and speeds up the convergence.
蓝吕盈, 唐向红, 顾鑫, 陆见光. 基于乌鸦搜索优化BP神经网络的入侵检测方法[J]. 激光与光电子学进展, 2021, 58(6): 0610006. Lan Lüying, Tang Xianghong, Gu Xin, Lu Jianguang. Intrusion Detection Method of BP Neural Network Based on Crow Search Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(6): 0610006.