IoT Terminal Risk Assessment Model Based on Improved CAE
王君艳 1伊鹏 2贾洪勇 1张建辉3
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作者信息
1. 郑州大学网络空间安全学院,郑州 450001
2. 中国人民解放军战略支援部队信息工程大学信息技术研究所,郑州 450001
3. 郑州大学网络空间安全学院,郑州 450001;嵩山实验室,郑州 450001
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摘要
物联网异构终端数量大、结构简单、安全防护能力弱,容易成为攻击目标.针对传统风险评估方法处理不断变化的大量风险因素时,评估机制建立困难,评估效率不高的问题,文章提出基于改进卷积自动编码器的物联网终端风险评估模型(Lightweight Convolutional Autoencoder Combined with Fully Connected Layers and Classifier Model,LCAE-FC).将更轻量化卷积自动编码器与分类器结合构建模型,使高维特征学习与逐阶降维输出评估概率值一体化;编码器引入深度可分离卷积,每个通道学习广义行为特征内部结构;每个输出特征均进行平均池化,最大限度保留风险信息;全连接层与分类器结合将高维特征抽象后阶梯式降维输出风险概率值.N-BaIoT数据集上的实验结果显示,文章所提模型精确度和F1值均高达99.3%以上,相较传统的CAE、Bi-LSTM和SAE-SBR模型,性能更优.
Abstract
The number of heterogeneous terminals in the Internet of Things is large,the structure is simple,the security protection ability is weak,and it is easy to become the target of attack.Aiming at the difficulties in establishing the evaluation mechanism and low evaluation efficiency when traditional risk assessment methods deal with a large number of changing risk factors,a risk assessment model of IoT terminal based on improved convolutional autoencoder was proposed(Lightweight Convolutional Autoencoder combined with Fully Connected Layers and Classifier Model,LCAE-FC).A lightweight convolutional encoder was combined with a classifier to build a model,which integrated high-dimensional feature learning with the output evaluation probability of order dimensional reduction.The encoder introduced deep separable convolution,and each channel learned the internal structure of generalized behavioral risk.Each output feature was averaged and pooled to retain risk information to the maximum extent.The risk probability value was output by step-dimensionality reduction after the high-dimensional features were abstracted by the fully connected layer and classifier.The experimental results on the N-BaIoT dataset show that the accuracy and F1 value of the proposed model are higher than 99.3%,which has better performance than the traditional CAE,Bi-LSTM and SAE-SBR models.
关键词
物联网终端/风险评估/卷积自动编码器/广义行为风险因素/深度可分离卷积
Key words
internet of things terminal/risk assessment/convolutional automatic encoder/broad behavioral risk factors/depth-separable convolution
维普
万方数据