Context:Predicting function names in stripped binaries is a critical challenge in reverse engineering due to their lack of explicit semantic information. Existing machine learning approaches often suffer from poor gen...
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Entity and relation extraction is a basic task of information extraction in natural language processing. At present, Entity and relation extraction based on artificial intelligence has been widely studied, but most me...
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Network intrusion detection is an important technology for maintaining cybersecurity. The inherent difficulties co-existing in network traffic datasets, such as class imbalance, class overlapping, and noises, limit de...
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With the metaverse being the development direction of the next generation Internet,the popularity of intelligent devices,and the maturity of various emerging technologies,more and more intelligent devices try to conne...
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With the metaverse being the development direction of the next generation Internet,the popularity of intelligent devices,and the maturity of various emerging technologies,more and more intelligent devices try to connect to the Internet,which poses a major threat to the management and security protection of network *** present,the mainstream method of network equipment identification in the metaverse is to obtain the network traffic data generated in the process of device communication,extract the device features through analysis and processing,and identify the device based on a variety of learning *** methods often require manual participation,and it is difficult to capture the small differences between similar devices,leading to identification ***,we propose a deep learning device recognition method based on a spatial attention ***,we extract the required feature fields from the acquired network traffic ***,we normalize the data and convert it into grayscale *** that,we add a spatial attention mechanism to CNN and MLP respectively to increase the difference between similar network devices and further improve the recognition ***,we identify devices based on the deep learning model.A large number of experiments were carried out on 31 types of network devices such as web cameras,wireless routers,and *** results show that the accuracy of the proposed recognition method based on the spatial attention mechanism is increased by 0.8%and 2.0%,respectively,compared with the recognition method based only on the deep learning model under the CNN and MLP *** method proposed in this paper is significantly superior to the existing method of device-type recognition based only on a deep learning model.
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