基于文件分时索引的大规模流量实时IoT终端识别算法

Real-Time IoT Terminal Identification Algorithm for Large-Scale Flow Based on Time-Sharing Index of Files

随着5G时代的来临,诸如工业区,校园网等开放性园区网络中存在大量的物联网(Internet of Things,IoT)终端,IoT终端由于其数据流量巨大,伪造IoT终端进行网络攻击的问题日益严重.现有IoT终端识别技术在面对海量数据时计算资源的成本逐渐提高.针对以上问题,提出了基于文件分时索引的大规模流量实时IoT终端识别算法.首先,建立内存分时索引元数据;其次,使用文件的分时索引来存储构建会话的中间数据;最后,控制内存分时索引元数据触发从少量文件中提取特征并进行IoT终端识别.实验中,在不损失IoT终端识别算法精度条件下,仅消耗少量磁盘,可将内存消耗降低92%.实验结果表明,该技术能够用于实时IoT终端识别框架中.

With the advent of the 5G era,there exist a large number of Internet of Things(IoT)terminals in the open campus network such as industrial area and campus network.Due to the huge data flow of IoT terminals,the problem of counterfeiting IoT terminals for network attack becomes increasingly serious,and the cost of computing resources of the existing IoT terminals identification technologies in the face of massive data increases gradually.To solve these problems,we propose a real-time IoT terminals identification algorithm for large-scale flow based on the time-sharing index of files.Firstly,the metadata for the time-sharing index of memory is established.Secondly,the time-sharing index of files is used to store the intermediate data of the construction session.Thirdly,the metadata trigger for the time-sharing index of memory is controlled to extract features from a small number of files and perform IoT terminals identification.In the experiment,on the premise of maintaining the accuracy of the IoT terminals identification algorithm,only a little disk space is occupied and the memory consumption is reduced by 92%.These results show that the proposed algorithm can be used in the framework of real-time IoT terminals identification.