摘要
通过协议特征对联网终端进行远程的类型推断、厂商与型号的有效识别,是实现网络安全测评的重要基础。以识别和推断联网设备的类型为目标,基于物联网终端WEB管理页面,利用信息增益模型提取特定类型终端的特征,提出正样本反馈增强的PU学习方法(FE-PU),进而形成从网络空间的海量设备中过滤特定类型物联网终端的一般方法。通过对100万网络空间联网终端的WEB管理页面中抽取视频监控设备的实验,表明该方法较直接采用PU学习方法的准确率和召回率都有大幅提升,较人工方法召回率也提升超过10%,且能够有效发现小品牌终端设备。
Remote recognition of type, brand and model of cyberspace terminals is an important basis for evaluating the network security. Machine learning is the most applicable method for classifying the specific and other types of devices. Many responses from IOT devices can be used as learning sources, and management webpage is the most valuable of them. The enhanced feedback mechanism is introduced in a classical PU learnning algorithm, FE-PU, and a general-type recognizer framework also designed, which extracts various features with infor-gain model. Numerous experiments, the extraction of ip camera devices from one million devices, indicate that the proposed method can improve precision and recall rate obviously as compared with general PU method, and is better than manual method by over 10%, and could also find lots of niche products which are always ignored by other or manual regnition algorithm.
引文
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