CNN Based Malicious Website Detection by Invalidating Multiple Web Spams
| Autor: | Dongjie Liu, Jong-Hyouk Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Class (computer programming)
General Computer Science Computer science business.industry malicious website detection Feature extraction General Engineering 020206 networking & telecommunications Convolutional neural network 02 engineering and technology computer.software_genre Spamdexing Support vector machine machine learning Web page 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science The Internet Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 97258-97266 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Although a variety of techniques to detect malicious websites have been proposed, it becomes more and more difficult for those methods to provide a satisfying result nowadays. Many malicious websites can still escape detection with various Web spam techniques. In this paper, we first summarize three types of Web spam techniques used by malicious websites, such as redirection spam, hidden IFrame spam, and content hiding spam. We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. In order to verify the effectiveness of the method, two different experiments have been conducted. First, the proposed method was tested based on a constructed complex dataset. We present comparison results between the proposed method and representative machine learning-based detection algorithms. Second, the proposed method was tested to detect malicious websites in a real-world Web environment for three months. These experimental results illustrate that the proposed method has a better performance and is applicable to a practical Web environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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