Bài báo quốc tế
Real-time phishing detection using deep learning methods by extensions
Đàm Minh Lịnh
Phishing is an attack method that relies on a user’s insufficient vigilance and
understanding of the internet. For example, an attacker creates an online
transaction website and tricks users into logging into the fake website to
steal their personal information, such as credit card numbers, email
addresses, phone numbers, and physical addresses. This paper proposes
implementing an extension to prevent phishing for internet users. In
particular, this study develops a smart warning feature for the proposed
extension using deep learning models. The proposed extension installed in
the web browser protects users by checking for, warning about, and
preventing untrusted connections. This study evaluated and compared the
performance of machine learning models using a malicious uniform resource
locator (URL) dataset containing 651,191 data samples. The results of the
investigation confirm that the proposed extension using a convolutional
neural network (CNN) achieved a high accuracy of 98.4%.
Xuất bản trên:
Real-time phishing detection using deep learning methods by extensions
Nhà xuất bản:
International Journal of Electrical and Computer Engineering (IJECE)
Địa điểm:
Từ khoá:
Anti-phishing; Character embedding; Cybersecurity; Deep learning; Extension; Malicious URLs; Phishing detection
Bài báo liên quan
Optimizing Resource Allocation for Dynamic IoT Requests Using Network Function Virtualization
Phạm Tuấn MinhExploring Linguistic Patterns through Machine Learning: Evidence from Logistic Regression Analysis
Nguyễn Minh TuấnEffective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection
Huỳnh Trọng Thưa