基于LCNN和LSTM混合结构的物联网设备识别方法  

IoT Device Identification Method Based on LCNN and LSTM Hybrid Structure

作　　者：李志华 王志豪 LI Zhihua;WANG Zhihao(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学人工智能与计算机学院,无锡214122

出　　处：《信息网络安全》2023年第6期43-54,共12页Netinfo Security

基　　金：国家自然科学基金[60704047];工业和信息化部智能制造项目[ZH-XZ-180004];中央高校基本科研业务费专项资金[JUSRP211A41,JUSRP42003];111基地建设项目[B2018]。

摘　　要：随着物联网设备数量的与日俱增,物联网环境中网络流量的规模也随之剧增,为了从海量的网络流量中高效地实现物联网设备的识别和分类,文章提出一种物联网设备识别方法。首先,为了消除网络流量中不规范的数据样本,研究并提出一种基于滑动窗口的数据预处理(Sliding Window-Based Data Pre-Processing,SW-Based DPP)算法,使用SW-Based DPP算法对数据进行清洗;然后,为了降低物联网设备识别方法的复杂度,通过把轻量级卷积神经网络(Lightwight Convolution Neural Network,LCNN)和LSTM结构进行结合,提出一种基于LCNN-LSTM混合结构的神经网络模型;接着,将数据预处理后的网络流量输入到LCNN-LSTM模型中进行物联网设备分类;最后,基于上述混合结构的神经网络模型,进一步提出一种基于LCNN和LSTM混合结构的物联网设备识别(Internet of Things Devices Identification Based on LCNN and LSTM Hybrid Structure,LCNN-LSTM-Based IoTDI)方法。该方法通过迭代训练LCNN-LSTM模型,深度挖掘网络流量中的时间和空间双重特征,并使用softmax分类器实现物联网设备识别的目标。实验结果表明,在UNSW、CIC IoT和Laboratory数据集上,LCNN-LSTM模型的运行时间与CNN-LSTM模型相比平均降低了约47.63%,并且LCNN-LSTM-Based IoTDI方法的F1值分别为88.6%、95.6%和99.7%。证明了该方法具有高效的设备识别能力。With the increasing number of IoT devices,the scale of network traffic in IoT environments has also skyrocketed.In order to efficiently identify and classify IoT devices from massive network traffic,this paper proposed a IoT devices recognition method.Firstly,in order to eliminate non-standard data samples in network traffic,a sliding window based data pre processing(SW based DPP)algorithm is studied and proposed,which uses the SW based DPP algorithm to clean the data;Then,in order to reduce the complexity of IoT devices recognition methods,a lightweight convolutional neural network(LCNN)was proposed,and a neural network model based on LCNN-LSTM hybrid structure was proposed by combining LCNN and LSTM structures;Input the preprocessed network traffic into the LCNN-LSTM model for IoT devices classification;Finally,based on the aforementioned hybrid structure neural network model,a further Internet of Things Devices Identification based on LCNN and LSTM Hybrid Structure(LCNN-LSTM-based IoTDI)method was proposed.The LCNN-LSTM-Based IoTDI method iteratively traind the LCNN-LSTM model to deeply mine the temporal and spatial dual features in network traffic,and used a softmax classifier to achieve the goal of IoT devices recognition.The experimental results show that on the UNSW,CIC IoT,and Laboratory datasets,the running time of the LCNN-LSTM model decreased by an average of about 47.63%compared to the CNN-LSTM model,and the F1 values of the LCNN-LSTM-Based IoTDI method are 88.6%,95.6%and 99.7%.It has been proven that the LCNN-LSTM-Based IoTDI method has efficient devices recognition capabilities.

关 键 词：物联网 设备识别 卷积神经网络 长短期记忆 网络流量 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]