With the continuous development of Web attacks, many web applications have been suffering from various forms of security threats and network attacks. The security detection of URLs has always been the focus of Web security. Many web application resources can be accessed by simply entering an URL or clicking a link in the browser. An attacker can construct various web attacks such as SQL, XSS, and information disclosure by embedding executable code or injecting malicious code into the URL. Therefore, it is necessary to improve the reliability and security of web applications by accurately detecting malicious URLs. This paper designs a convolutional gated-recurrent-unit (GRU) neural network for the detection of malicious URLs detection based on characters as text classification features. Considering that malicious keywords are unique to URLs, a feature representation method of URLs based on malicious keywords is proposed, and a GRU is used in place of the original pooling layer to perform feature acquisition on the time dimension, resulting in high-accuracy multicategory results. The experimental results show that our proposed neural network detection model is very suitable for high-precision classification tasks. Compared with other classification models, the model accuracy rate is above 99.6%. The use of deep learning to classify URLs to identify Web visitors' intentions has important theoretical and scientific values for Web security research, providing new ideas for intelligent security detection. INDEX TERMS Gated recurrent unit (GRU), malicious URL detection, network attack, character-level embedding, convolutional neural network, neural network model.
Keystroke rhythm identification, which extracts biometric characteristics through keyboards without additional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identity authentication model based on the improved keystroke rhythm algorithm in Rick Joyce model and implement this model in a mobile phone system. The experimental results show that comparing with the original model, the false alarm rate (FAR) of the improved model decreases a lot in the mobile phone system, and its growth of imposter pass rate (IPR) is slower than the Rick Joyce model's. The improved model is more suitable for small memory systems, and it has better performance in security and dynamic adaptation. This improved model has good application value.
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