Unstructured Data Analysis of Communication Network Based on Deep Learning

Abstract

This goal has continued to drive academia and industry to investigate, design and develop different solutions to reduce the risk of data breaches. With the increasing demand for large labeled datasets, there is an urgent need for accurate and efficient evaluation of unstructured packet annotations. Second, some non-independent reliability indicators are used repeatedly, exaggerating the determinant role of this indicator on the target. Therefore, it is necessary to estimate the interactive traffic, and through the traffic estimation, relevant data such as the destination of the effective information flow, the traffic attributes, and the legality of the traffic can be obtained. The application of deep learning and natural language processing techniques to automatically identify user confusion in unstructured crowdsourced data labels is explored. The dataset contains images, computer-generated questions for each image, and responses from social media users. Existing data-driven techniques mainly employ traditional shallow learning models, such as Bayesian networks, neural networks, etc. However, it is usually suitable for abnormal data types with obvious characteristics and has poor universality. Mass data information interacts in the form of data traffic, and the information content and its security are directly related to the security of the entire communication system. Therefore, traditional desensitization of unstructured data mainly focuses on the network security rights control, access control and other means, which directly prohibits the leakage of unstructured data. Data extraction can be understood as the acquisition of non-defective semantic data in unstructured tables. Previous data extraction methods were mainly collected manually, which resulted in a large number of subjectivity, resulting in lower extraction accuracy and limited extraction range. The migration between different desensitization systems is poor, and the consequence of mode rigidity is that the desensitization results in a greater loss of data information. The in-depth learning model first reduces the dimension of the reliability index, then predicts the reliability based on the reliability characteristics after dimension reduction. In this paper, the in-depth learning model can be used to analyze unstructured data of communication network and complete reliability prediction in actual operation data.