@Article{cmc.2020.06507,
AUTHOR = {Chaochao Luo, Shen Su, Yanbin Sun, Qingji Tan, Meng Han, Zhihong Tian},
TITLE = {A Convolution-Based System for Malicious URLs Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {1},
PAGES = {399--411},
URL = {http://www.techscience.com/cmc/v62n1/38120},
ISSN = {1546-2226},
ABSTRACT = {Since the web service is essential in daily lives, cyber security becomes more
and more important in this digital world. Malicious Uniform Resource Locator (URL) is
a common and serious threat to cybersecurity. It hosts unsolicited content and lure
unsuspecting users to become victim of scams, such as theft of private information,
monetary loss, and malware installation. Thus, it is imperative to detect such threats.
However, traditional approaches for malicious URLs detection that based on the
blacklists are easy to be bypassed and lack the ability to detect newly generated malicious
URLs. In this paper, we propose a novel malicious URL detection method based on deep
learning model to protect against web attacks. Specifically, we firstly use auto-encoder to
represent URLs. Then, the represented URLs will be input into a proposed composite
neural network for detection. In order to evaluate the proposed system, we made
extensive experiments on HTTP CSIC2010 dataset and a dataset we collected, and the
experimental results show the effectiveness of the proposed approach.},
DOI = {10.32604/cmc.2020.06507}
}