A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security
Year: 2022
Type: article
Abstract: Summary Uniform resource locator (URL)‐based cyber‐attacks form a major part of security threats in cyberspace. Even though the experience and awareness of the end‐users help them protect themselves from these attacks, a software‐based solution is necessary for comprehensive protection. To this end, a novel robust URL classification model based on convolutional neural network is proposed in this study. The proposed model classifies given URLs into five classes, namely, () , () , () , () , and () . The proposed model was trained and evaluated on a gold standard URL dataset comprising of samples. According to the experimental result, the proposed model obtained an accuracy as high as which outperformed the state‐of‐the‐art. Based on the same architecture, we proposed another classifier, a binary classifier that detects malicious URLs without dealing with their types. This binary classifier obtained an accuracy as high as which outperformed the state‐of‐the‐art as well. The experimental result demonstrates the feasibility of the proposed solution.
DOI: 10.1002/cpe.7517
Author: Abdullah Talha Kabakus
Institution: Düzce Üniversitesi
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4309556340