Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Author:

Mustafa Hilal Anwer,Hassan Abdalla Hashim Aisha,G. Mohamed Heba,K. Nour Mohamed,M. Asiri Mashael,M. Al-Sharafi Ali,Othman Mahmoud,Motwakel Abdelwahed

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modeling and Simulation,Biomaterials

Reference25 articles.

1. Towards detecting and classifying malicious URLs using deep learning;Johnson;Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications,2020

2. Evaluating deep learning approaches to characterize and classify malicious URL’s;Vinayakumar;Journal of Intelligent & Fuzzy Systems,2018

3. Robust detection of malicious Robust detection of malicious URLs with self-paced wide & deep learning;Liang;IEEE Transactions on Dependable and Secure Computing,2021

4. A deep learning approach for detecting malicious JavaScript code: Using a deep learning approach to detect JavaScript-based attacks;Wang;Security and Communication Networks,2016

5. Analysis for malicious URLs using machine learning and deep learning approaches;Birthriya,2021

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An ensemble classification method based on machine learning models for malicious Uniform Resource Locators (URL);PLOS ONE;2024-05-31

2. Detecting Phishing URLs With Word Embedding and Deep Learning;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2023-06-30

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