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Dongmei Zhao

Bio: Dongmei Zhao is an academic researcher from Hebei Normal University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has co-authored 1 publications.

Papers
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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

Journal ArticleDOI
TL;DR: In this article , the authors combine ResNeXt with Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions.
Abstract: The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.

1 citations

Book ChapterDOI
TL;DR: In this article , the Contextual Transformer (COT) block and Efficient Channel Attention (ECA) module were combined with ResNeXt and Harris Hawks Optimization (HHO) algorithm to choose the most suitable hyperparameters to improve the model performance.
Abstract: Because the traditional convolutional neural network (CNN) cannot obtain the importance of each channel and its receptive field is limited, it is difficult to deal with the increasingly complex network environment. Aiming at the shortcomings, this paper combines the Contextual Transformer (COT) block and the Efficient Channel Attention (ECA) module with ResNeXt and uses Harris Hawks Optimization (HHO) algorithm to choose the most suitable hyperparameters to improve the model performance. This model enables the rich contexts among neighbor keys to be fully exploited and can obtain the importance of each channel to improve the weight of the useful channel and suppress the less useful channel, effectively making up for the shortcomings of the traditional convolutional neural network. The experiments on a network security dataset show that the model is superior to other models in the network security situation assessment effect and its comprehensive performance in accuracy, precision and F-score.

1 citations

Proceedings ArticleDOI
01 Sep 2022
TL;DR: In this paper , the preventive methods for input verification, output coding, clearly specifying the coding mode of output, paying attention to the limitations of blacklist verification mode, being alert to standardization errors, and giving examples of not inserting untrusted data in the allowed position, and HTML coding these data when inserting unsafe data between HTML tags.
Abstract: The international student website is the window of the school's external publicity. Through the website, international students can understand the school in a wider range and in all aspects. The website is often attacked by network hackers. How to ensure the security of the website? Cross site scripting attack (XSS) is the most common web application security vulnerability. Through the analysis of cross site scripting attack (XSS), this paper puts forward the preventive methods for input verification, output coding, clearly specifying the coding mode of output, paying attention to the limitations of blacklist verification mode, being alert to standardization errors, and gives examples of not inserting untrusted data in the allowed position, and HTML coding these data when inserting untrusted data between HTML tags, Before inserting untrusted data into common HTML attributes, encode HTML attributes; before inserting untrusted data into HTML JavaScript, encode JavaScript; before inserting untrusted data into HTML style attribute values, encode CSS; before inserting untrusted data into HTML URL attributes, encode URL; when using rich text, use XSS rule engine to encode and filter and other corresponding defense rules.
Journal ArticleDOI
27 Feb 2023-Sensors
TL;DR: In this article , the authors present a state-of-the-art study on network security situation awareness (NSSA) that can help bridge the current research status and future large-scale application.
Abstract: Network security situation awareness (NSSA) is an integral part of cybersecurity defense, and it is essential for cybersecurity managers to respond to increasingly sophisticated cyber threats. Different from traditional security measures, NSSA can identify the behavior of various activities in the network and conduct intent understanding and impact assessment from a macro perspective so as to provide reasonable decision support, predicting the development trend of network security. It is a means to analyze the network security quantitatively. Although NSSA has received extensive attention and exploration, there is a lack of comprehensive reviews of the related technologies. This paper presents a state-of-the-art study on NSSA that can help bridge the current research status and future large-scale application. First, the paper provides a concise introduction to NSSA, highlighting its development process. Then, the paper focuses on the research progress of key technologies in recent years. We further discuss the classic use cases of NSSA. Finally, the survey details various challenges and potential research directions related to NSSA.

Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors combine ResNeXt with Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions.
Abstract: The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.

1 citations

Proceedings ArticleDOI
04 May 2022
TL;DR: In this paper , a network security situation prediction method based on Attention-CNN-BiGRU is proposed, which combines CNN and BiGRU for extraction and understanding of situation time series.
Abstract: To improve the accuracy and efficiency of network security situation prediction, a network security situation prediction method based on Attention-CNN-BiGRU is proposed. It combined Convolutional Neural Network(CNN) and bidirectional Gated Recurrent Unit(BiGRU), The structure of the proposed model is more accurate for extraction and understanding of situation time series; besides, to improve the performance of proposed model, the Attention mechanism is utilized to optimize the model; particle swarm optimization(PSO) is applied to optimize varieties of hyperparameters of the model. The optimal combination of hyperparameters selected by PSO is used for training of the model. Finally, the method proposed in this paper was verified in experiment and compared with other models. The results shows that the method can acomplish the task of situation prediction with better capability.

1 citations

Journal ArticleDOI
01 Jul 2023-Sensors
TL;DR: In this paper , the authors proposed an optimized clock-cycle recurrent neural network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and realtime performance.
Abstract: We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model. The introduction of a clock-cycle for hidden units allows the model to learn short-term information from high-frequency update modules while retaining long-term memory from low-frequency update modules, thereby enhancing the model’s ability to capture data patterns. Experimental results demonstrate that the optimized clock-cycle RNN outperforms other network models in extracting the temporal and nonlinear features of network security situations, leading to improved prediction accuracy. Furthermore, our approach has low time complexity and excellent real-time performance, ideal for monitoring large-scale network traffic in sensor networks.