4.7 Article

CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Information Systems

GramBeddings: A New Neural Network for URL Based Identification of Phishing Web Pages Through N-gram Embeddings

Ahmet Selman Bozkir et al.

Summary: In this study, a new deep neural model for phishing URL identification is proposed, which introduces some novel features. A large-scale and novel dataset is also provided, and the superiority of the model is verified through comparative studies. The robustness of the model is tested against real-world adversarial attacks, and the codebase is shared for future benchmarking purposes.

COMPUTERS & SECURITY (2023)

Article Computer Science, Information Systems

HDP-CNN: Highway deep pyramid convolution neural network combining word-level and character-level representations for phishing website detection

Faan Zheng et al.

Summary: Phishing is a prevalent method for attackers to steal users' private data and commit fraud, posing a serious threat to Internet users. Traditional methods rely on support vector machine (SVM) and expert-designated feature extraction, which cannot handle unidentifiable features. To address this, a novel deep convolutional network (HDP-CNN) is proposed, which combines character-level and word-level representation information to better detect phishing websites.

COMPUTERS & SECURITY (2022)

Article Computer Science, Artificial Intelligence

TCURL: Exploring hybrid transformer and convolutional neural network on phishing URL detection

Chenguang Wang et al.

Summary: Phishing is a growing threat where cybercriminals create fake websites to deceive victims and obtain sensitive information. Traditional methods struggle to combat new phishing attacks, but the proposed hybrid network architecture TCURL shows improved detection accuracy.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Information Systems

A lightweight data representation for phishing URLs detection in IoT environments

Lazaro Bustio-Martinez et al.

Summary: Phishing is a cyber-attack that preys on victims' technical ignorance or naivety and is now increasingly targeting the Internet of Things (IoT) field. While there have been approaches for detecting phishing attacks, research on selecting the most suitable feature set for IoT environments is limited.

INFORMATION SCIENCES (2022)

Article Telecommunications

A comprehensive survey of AI-enabled phishing attacks detection techniques

Abdul Basit et al.

Summary: Phishing attacks have become a major threat to internet users, governments, and service organizations, where attackers collect sensitive information through spoofed emails and fake websites. Phishing websites serve as common entry points for online social engineering attacks, with attackers mimicking legitimate websites to deceive victims. Research on using artificial intelligence techniques for detecting phishing attacks is discussed, along with an examination of current challenges and future research directions in the field.

TELECOMMUNICATION SYSTEMS (2021)

Article Computer Science, Information Systems

AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification

Suyeon Yoo et al.

Summary: The highly distributed architecture of the Internet facilitates the spread of malware, posing a significant challenge for defense mechanisms. This paper proposes a machine learning-based hybrid decision model that achieves a high detection rate with a low false positive rate, as demonstrated in experiments with atypical samples.

INFORMATION SCIENCES (2021)

Article Computer Science, Information Systems

Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection

Seok-Jun Bu et al.

Summary: The study investigates the data-driven and supervised learning approaches for phishing attacks in cyber security. A novel method combining convolutional operation and deep convolutional autoencoder is proposed, showing superior performance compared to the latest deep-learning methods.

ELECTRONICS (2021)

Review Computer Science, Interdisciplinary Applications

Phishing Attacks: A Recent Comprehensive Study and a New Anatomy

Zainab Alkhalil et al.

Summary: With the rapid growth of internet usage, phishing attacks have become more prevalent, posing significant risks and losses to users. Research on phishing attacks should focus on the entire attack lifecycle and propose appropriate preventive measures.

FRONTIERS IN COMPUTER SCIENCE (2021)

Article Computer Science, Information Systems

An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

Ali Aljofey et al.

ELECTRONICS (2020)

Article Computer Science, Artificial Intelligence

Machine learning based phishing detection from URLs

Ozgur Koray Sahingoz et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Computer Science, Information Systems

A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

Kang Leng Chiew et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Information Systems

PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks

Weiping Wang et al.

SECURITY AND COMMUNICATION NETWORKS (2019)

Article Computer Science, Information Systems

Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning

Peng Yang et al.

IEEE ACCESS (2019)

Review Computer Science, Artificial Intelligence

Fighting against phishing attacks: state of the art and future challenges

B. B. Gupta et al.

NEURAL COMPUTING & APPLICATIONS (2017)

Article Computer Science, Information Systems

Social network security: Issues, challenges, threats, and solutions

Shailendra Rathore et al.

INFORMATION SCIENCES (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Computer Science, Information Systems

PhishStorm: Detecting Phishing With Streaming Analytics

Samuel Marchal et al.

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT (2014)