Research on moving object detection based on the Bayesian theory

Hu, Di; Liu, X.

doi:10.2495/isme20132412

Cited by 1 publication

(1 citation statement)

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“…However, there are fewer training samples used in this document. In [3], a Webshell detection method based on Naive Bayesian theory is proposed for Webshell with obfuscated encryption coding technology. This model can accurately detect Webshells that have been confusingly encrypted and encoded, effectively improving traditional feature-based detection methods.…”

Section: Static Feature Detectionmentioning

confidence: 99%

Automatic and Accurate Detection of Webshell Based on Convolutional Neural Network

Yan

Rui

2019

Communications in Computer and Information Science

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The rapid development of the Internet has changed the way people live and work. Web security, as the foundation of network security, has received much more attention. Based on the variability of Webshells and the vulnerability of detection methods, this paper proposed a model that used deep learning to detect and implements the automatic identification of Webshells. For the shortcomings of the traditional detection models using machine learning algorithms, this paper proposed to apply convolutional neural network to Webshell detection process. The deep learning model does not require complicated artificial feature engineering, and the modeled features trained through model learning can also allow the attacker to avoid targeted bypassing in Webshell detection. The experimental results showed that this method not only has better detection accuracy, but also can effectively avoid the attacker's targeted bypassing. At the same time, with the accumulation of training samples, the detection accuracies of the detection model in different application environments will gradually improvements, which has clear advantages over traditional machine learning algorithms.

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Section: Static Feature Detectionmentioning

confidence: 99%