基于加权特征融合的物联网设备识别方法  

IoT Devices Identification Method Based on Weighted Feature Fusion

作　　者：曹伟康 林宏刚 CAO Weikang;LIN Honggang(School of Cyber Security(Xin Gu Industrial College),Chengdu University of Information Technology,Chengdu 610225,China;Sichuan Provincial Key Laboratory of Advanced Cryptography and System Security,Chengdu 610225,China;Anhui Key Laboratory of Cyberspace Security Situational Awareness and Assessment,Hefei 230037,China)

机构地区：[1]成都信息工程大学网络空间安全学院(芯谷产业学院),成都610225 [2]先进密码技术与系统安全四川省重点实验室,成都610225 [3]网络空间安全态势感知与评估安徽省重点实验室,合肥230037

出　　处：《计算机科学》2024年第S02期875-883,共9页Computer Science

基　　金：国家242信息安全计划项目(2021-037);网络空间安全态势感知与评估安徽省重点实验室课题(CSSAE-2021-002)。

摘　　要：物联网设备识别在设备管理和网络安全等领域具有极为重要的作用,它不仅有助于管理员及时审查网络资产,还能将设备信息与潜在漏洞信息相互关联,及时发现潜在的安全风险。目前的物联网设备识别方法存在没有充分利用物联网设备的特征,并且在样本不平衡的情况下难以识别出样本较少的设备等问题。针对上述问题,文中提出了一种基于加权特征融合的物联网设备识别方法,设计了TextCNN-BiLSTM_Attention并行结构,分别提取物联网设备应用层服务信息的局部特征和上下文特征;提出了一种加权特征融合算法对不同模型提取的特征进行融合;最后采用多层感知机完成设备识别。实验结果表明,该方法能更全面地提取物联网设备特征,在数据不平衡的情况下识别出样本较少的设备,宏平均精准率比现有方法提升了2.6%~12.85%,具有良好的表征能力和泛化能力,且在识别效率方面优于CNN_LSTM等多模型方法。IoT device identification plays an extremely important role in the field of device management and network security,which not only helps administrators review network assets in a timely manner,but also correlates device information with potential vulnerability information to discover potential security risks in a timely manner.The current IoT device identification methods do not make full use of the characteristics of iot devices,and it is difficult to identify devices with fewer samples in the case of unbalanced samples.To solve the above problems,this paper proposes a weighted feature fusion based method for IoT device recognition.A parallel structure of TextCNN-BiLSTM_Attention is designed to extract the local features and context features of the application layer service information of networked devices respectively.A weighted feature fusion algorithm is proposed to fuse the features extracted from different models.Finally,multi-layer perceptron is used to recognize the device.Experimental results show that the proposed method can extract the features of networked devices more comprehensively,identify devices with fewer samples under the condition of data imbalance,and the macro average precision rate is improved by 2.6%~12.85%compared with the existing methods,which has good characterization and generalization abilities,and is superior to multi-model methods such as CNN_LSTM in recognition efficiency.

关 键 词：物联网 设备识别 TextCNN BiLSTM_Attention 特征提取 加权特征融合 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]