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基于改进SKNet-SVM的网络安全态势评估

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为提高网络安全态势评估的准确率,增强稳定性与鲁棒性,提出一种基于改进选择性卷积核卷积神经网络和支持向量机的网络安全态势评估模型.首先,使用改进选择性卷积核代替传统卷积核进行特征提取,提高卷积神经网络感受野变化的自适应性,增强特征之间关联性.然后,将提取的特征输入到支持向量机中进行分类,并使用网格优化算法对支持向量机中的参数进行全局寻优.最后,根据网络攻击影响指标计算网络安全态势值.实验表明,基于改进选择性卷积核卷积神经网络和支持向量机的态势评估模型与传统的卷积神经网络搭建的态势评估模型相比,准确率更高,并且具有更强的稳定性和鲁棒性.
Network Security Situation Assessment Based on Improved SKNet-SVM
In order to improve the accuracy,stability,and robustness of network security situation assessment,a network security situation assessment model based on improved se-lective kernel convolutional neural network and support vector machine is proposed.Firstly,the traditional kernel for feature extraction is replaced with the improved selective kernel to enhance the adaptability of the convolutional neural network to changes in receptive field,thereby strengthening the correlation between features.Then,the extracted features are fed into the support vector machine for classification,and the grid optimization algorithm is used to optimize the parameters in the support vector machine globally.Finally,the net-work security situation value is calculated according to the network attack impact index.Experimental results show that the situation assessment model based on improved selective kernel convolutional neural network and support vector machine achieves higher accuracy,stronger stability and robustness compared to traditional convolutional neural networks.

network security situation assessmentnetwork security situation awarenessimproved selective kernel convolutional neural networksupport vector machinegrid opti-mization algorithm

赵冬梅、孙明伟、宿梦月、吴亚星

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河北师范大学计算机与网络空间安全学院,河北石家庄 050024

河北师范大学河北省网络与信息安全重点实验室,河北石家庄 050024

供应链大数据分析与数据安全河北省工程研究中心,河北石家庄 050024

网络安全态势评估 网络安全态势感知 改进选择性卷积核卷积神经网络 支持向量机 网格优化算法

国家自然科学基金中央引导地方科技发展资金河北省省级科技计划河北师范大学科研基金河北师范大学科研基金

61672206216Z0701G22567606HL2023J04L2021T09

2024

10.3969/j.issn.0255-8297.2024.02.014

应用科学学报
上海大学 中国科学院上海技术物理研究所

应用科学学报

CSTPCD北大核心
影响因子：0.594
ISSN：0255-8297
年,卷(期)：2024.42(2)
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