IEEE Access · 2019 · 105 citations · 23 references
ComputingComputer ScienceCybersecurityComputer SecurityMachine LearningWeb SecurityBrowser SecurityDeep LearningRepresentation LearningText Feature EngineeringThreat DetectionWeb Application SecurityClassification PrecisionConvolutional Neural Network DetectionDeep Learning DetectionFeature AcquisitionGated Recurrent Unit NetworksText ClassificationText Classification FeaturesUrl Analysis
Web applications continuously face security threats, with attackers exploiting URLs to deliver malicious code such as SQL injection, XSS, and information disclosure. The study aims to enhance web security by accurately detecting malicious URLs. A convolutional gated‑recurrent‑unit neural network that extracts character‑level features and uses malicious‑keyword‑based representation, replacing pooling with a GRU to capture temporal dependencies, is proposed for high‑accuracy multiclass URL classification. Experiments demonstrate the model achieves over 99.6% accuracy, proving its suitability for high‑precision malicious URL classification and offering valuable insights for intelligent web security.
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.
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Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 98.1K citations
Convolutional Neural Networks for Sentence Classification