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Qingru Li

Bio: Qingru Li is an academic researcher. The author has contributed to research in topics: Network security. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Journal ArticleDOI
TL;DR: Experiments prove that the framework built with the improved LSTM has better performance to predict network security situation in the near future.
Abstract: In recent years, raw security situation data cannot be utilized well by fully connected neural networks. Generally, a cyber infiltration is a gradual process and there are logical associations between future situation and historical information. Taking the factors into account, this paper proposes a framework to predict network security situation. According the needs of this framework, we improve Long Short-Term Memory (LSTM) with Cross-Entropy function, Rectified Linear Unit and appropriate layer stacking. Modules are designed in the framework to transform raw data into quantitative results. Finally, the performance is evaluated on KDD CUP 99 dataset and UNSW-NB15 dataset. Experiments prove that the framework built with the improved LSTM has better performance to predict network security situation in the near future. The framework achieves a relatively practical prediction of network security situation, helping provide advanced measures to improve network security.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , the authors used a deep learning model to predict the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia, and achieved a 98.58% precision and 93.45% precision, respectively.

43 citations

Journal ArticleDOI
TL;DR: In this article, the authors used a deep learning model to predict the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia, and achieved a 98.58% precision and 93.45% precision, respectively.

43 citations

Journal Article
TL;DR: From the views of problems that network security situation awareness needs to be resolved, its fundamental concept, model and framework are introduced in detail and its key technology and development are described in these fields of feature extraction, situation assessment, and situation prediction.

22 citations

Journal ArticleDOI
TL;DR: A systematic review of intelligent threat profiling techniques for APT attacks, covering three aspects: data, methods, and applications, is provided in this paper , which summarizes the latest research in applications, proposes the research framework and technical architecture, and provides insights into future research trends.

4 citations

Journal ArticleDOI
TL;DR: In this paper, a situation prediction method based on feature separation and dual attention mechanism is presented in order to improve the safety of smart cities, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged.
Abstract: With the development of smart cities, network security has become more and more important. In order to improve the safety of smart cities, a situation prediction method based on feature separation and dual attention mechanism is presented in this paper. Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. Then, we propose a feature separation method, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged. Finally, limited attention is proposed according to global attention. We sum the outputs of the two attention modules to form a dual attention mechanism, which can improve feature representation. Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. In other words, the technology can help smart cities predict network attacks more accurately.

1 citations