Assessing the Security of Campus Networks: The Case of Seven Universities

Mobile Information Systems

2022

Knowledge graph, as a structured semantic knowledge base, has become an essential foundation for artificial intelligence applications with its flexible composition structure and rich semantic representation capability. This paper combines the knowledge graph embedding scoring algorithm with the link scoring algorithm to effectively solve the problem of missing answers in the current knowledge graph embedding question and answer method. This method constructs a query link while searching for the best answer and gives the answer set through the query, which effectively alleviates the omission of answers in the existing methods. The experimental results show that the F1 score of the English teaching test system on the data set is 86.85%, where the answer selection method weakly relies on a priori information such as predicates in the test data and can be trained on a test pair data set without human intervention, with good generalization performance.

“…is paper selects literature [21][22][23][24][25][26][27][28]and literature [29] as the comparison method. e automatic question and answer results are shown in Table 6.…”

Section: Comparison Of Automatic Test Resultsmentioning

confidence: 99%

Section: Comparison Of Automatic Test Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Knowledge Graph Question Answering System for English Teaching

Mobile Information Systems

2022

“…e asset discovery technology based on cyberspace search engines mainly uses search engines to explore equipment and service information in the entire cyberspace [20,21]. Cyberspace search engines are different from traditional search engines such as Google, Baidu, Bing and other comprehensive information search engines.…”

Section: Discovery Technology Based On Cyberspace Searchmentioning

confidence: 99%

MAE-CAD: An IP-Based Core Network Asset Discovery Technology Based on Multiple Autoencoders

Chang

Security and Communication Networks

et al. 2022

With the continuous development of the network, the number of network assets continues to increase. Despite the convenience diversified network assets bring, it also poses new challenges to IP-based network asset management. Traditional asset discovery technologies mainly analyze network traffic, and detect relevant information (operating system, running software, etc.) of IP-based assets through methods such as active discovery, passive discovery, and discovery methods based on cyberspace search engines. These methods assign the same weight to all network IP-based network assets, and it is difficult to effectively analyze diversified network assets. In this paper, we propose the concept of IP-based core network assets, and collect the data of the relevant network assets based on this concept. Then, we construct a dataset and establish feature engineering for data preprocessing. As there is currently no relevant IP-based core network asset detection method, we propose an IP-based core network asset discovery technology based on pretraining of multiple autoencoders, MAE-CAD. The results show that our method can achieve 95.74% in Acc and 95.04% in F1 in the experimental environment (Acc = 98.11% and F1 = 97.16% in the actual network environment because of duplicate samples). In addition, MAE-CAD has excellent robustness. In an environment where the proportion of data is extremely unbalanced, when the IP-based core network asset data in the training set only accounts for 1/200 (0.5%), MAE-CAD can still obtain 92.91% in Acc and 91.57% in F1.

“…KG has a good development prospect in risk identification, assessment, early warning, prevention and control [39]. In terms of information security, KG can be used to assess the security risk of the network and discover potential safety hazards on time [40]. In the field of natural disasters, a typhoon disaster KG is constructed to show the distribution and disaster situation of typhoon disasters, which help people to avoid typhoon disasters [41].…”

Section: Kg and Its Applications In The Risk Fieldmentioning

confidence: 99%

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Zhang

Feng

et al. 2021

IJGI

The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.