Learning URL Embedding for Malicious Website Detection

doi:10.1109/TII.2020.2977886

Learning URL Embedding for Malicious Website Detection

2020-10-01

45

PRE

AI

DOI：10.1109/TII.2020.2977886

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摘要

En 中文

The emergence of artificial intelligence technology has promoted the development of the Internet of Things. However, this promising cyber technology can encounter serious security problems while accessing the internet. A malicious website can disguise itself as a normal website, and obtain users' private information. Thus, it is very important to detect malicious websites using tools such as machine learning (ML) algorithms, as these algorithms can help us to identify abnormal information hidden in the mass traffic more easily. Accordingly, many feature engineering tasks must be performed from memory, as a strong machine learning model is greatly improved with good features. In this article, we propose an unsupervised learning algorithm that learns URL embedding. We also explore some key parameters regarding a domain embedding model to obtain a good effect on domain features.

Keyword:

Feature extraction

Uniform resource locators

Data mining

Neural networks

Security

Machine learning

Malware

Feature engineering

machine learning (ML)

malicious websites

URL embedding (UE) model

Learning URL Embedding for Malicious Website Detection

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