摘要
近年来,网络安全检测已经取得了很大的进步。然而,网络迅速的发展、流量分布的变化和数据样本中的噪声等问题都对现有方法提出了很大的挑战。针对此,提出了基于非对称分解卷积的网络安全检测(Network Security Detection Based on Asymmetric Decomposed Convolution,ADC-NSD)方法。ADC-NSD方法根据对网络连接数据的训练与学习,生成区别常态与危险状态的安全检测模型,通过对卷积神经网络中的卷积核进行分解,完成对数据进行解析和检测。最后,以KDDCUP99为测试数据集,将ADC-NSD方法与其他机器学习方法进行比较。实验结果表明,ADC-NSD方法能有效地解决网络安全检测问题,总体精确率为98.72%,准确率为99.92%,召回率为94.61%,F1值为97.19%。
In recent years,network security detection has made great progress.However,the rapid development of communication networks,changes of the traffic distribution and the noise in data samples all pose great challenges to the existing network security detection methods.To solve this problem,an approach referred as Network Security Detection based on Asymmetric Decomposition Convolution(ADC-NSD)is proposed.ADC-NSD generates a security detection model to distinguish normal state and dangerous state according to the training and learning of network connection data,and then analyzes and checks the data through decomposing the convolution kernel of the convolution neural network(CNN).Finally,using KDDCUP99(KDD:Knowledge Discovery and Data Mining)dataset as testing dataset,ADC-NSD is measured against other machine learning algorithms.The results show that ADC-NSD could be well applied to network security detection.The overall accuracy rating is 98.72%,precision rate being 99.92%,recall rate being 94.61%and F1 score being 97.19%.
作者
冯仁君
吴吉
王震宇
景栋盛
FENG Renjun;WU Ji;WANG Zhenyu;JING Dongsheng(Suzhou Power Supply Branch,State Grid Jiangsu Electric Power Limited Company,Suzhou 215004,China)
出处
《软件工程》
2020年第10期8-11,共4页
Software Engineering
基金
江苏省高等学校自然科学研究项目重大项目(17KJA520004).
关键词
网络安全
安全检测
卷积神经网络
非对称分解卷积
network security
security detection
convolution neural network
asymmetric decomposed convolution
