The comparison of traditional machine learning and deep learning methods for malicious website detection

Hongen Chen; Zhenyuan Liang; Weinan Zhang

doi:10.1117/12.2624883

13 December 2021 The comparison of traditional machine learning and deep learning methods for malicious website detection

Hongen Chen, Zhenyuan Liang, Weinan Zhang

Author Affiliations +

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871G (2021) https://doi.org/10.1117/12.2624883
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Today, the Internet has become more and more popular in people’s daily life. People use the Internet to get what they need. However, there are many malicious websites on the Internet. A malicious website has various unhealthy content, such as defraud content, gambling content and false information. Therefore, it is very necessary to identify malicious websites in time to prevent them from cases with various potential harm for users. Previously, the strenuous and time-consuming manual selection was the most prevalent method for malicious website detection. However, with the rise of machine learning, people could build machine learning models to train based on hand-crafted features for this task. Although being much better than manual selection, machine learning models still require a number of hand-craft features and plenty of laboring work. Nevertheless, deep learning models developed in recent ten years save a lot of work by automatically extracting features from malicious websites and produce excellent results, gaining more and more attention from researchers. This paper uses machine learning for malicious website detection, namely Random Forest [1]. And we also use a deep learning method based on LSTM (i.e., Long Short-Term Memory) to train a malicious websites detection model. Then, in order to verify the accuracy of the two models, we used malicious URLs crawled on the Internet to verify our two models. In the end, we got the results. Through comparisons in various aspects, we find that when we use the random forest method, the accuracy of our model will achieve a better result, and the model can maintain high performance than the deep learning-based method.

Citation Download Citation

Hongen Chen, Zhenyuan Liang, and Weinan Zhang "The comparison of traditional machine learning and deep learning methods for malicious website detection", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871G (13 December 2021); https://doi.org/10.1117/12.2624883

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Machine learning

Data modeling

Internet

Performance modeling

Neural networks

Binary data

Computer science

Show All Keywords

Keywords/Phrases

Search In:

Publication Years