Malicious URL and Intrusion Detection using Machine Learning
Cyberattacks are becoming increasingly sophisticated and evolving danger to the Web users. Therefore, addressing the growing threat of cyberattacks and providing automated solutions became a necessity. The purpose of this paper is to use machine learning (ML) techniques for malicious websites detect...
Saved in:
| Main Author: | Hamza, Amr (author) |
|---|---|
| Other Authors: | Hammam, Farah (author), Abouzeid, Medhat (author), Ahmed, Mohammad Arsalan (author), Dhou, Salam (author), Aloul, Fadi (author) |
| Format: | article |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/11073/26366 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures
by: Irfan Ali Kandhro (17541876)
Published: (2023) -
Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System
by: Abualigah, Laith
Published: (2024) -
Explainable phishing website detection for secure and sustainable cyber infrastructure
by: Tanzila Kehkashan (20748842)
Published: (2025) -
Optimizing malicious website prediction: An advanced XGBoost-based machine learning model
by: Sumaira Hussain (19259669)
Published: (2025) -
Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach
by: Shaikh Afnan Birahim (22303750)
Published: (2025)