| An approach for Fack website detection |
| Paper ID : 1060-ISCBAS |
| Authors |
|
Aya Said Noah *1, Abeer Desuky2, Naglaa El-Sayed3, Gaber El-Sharawy3 1Al Azhar University - Faculty of science - Department of computer science and pure mathematics. 2Azhar Univ. 3Al-Azhar University (Girl’s branch), Mathematics Department, Cairo, 88813, Egypt |
| Abstract |
| Internet usage has significantly increased in recent years, which has resulted in data theft and a wide range of counterfeit goods. This has led to an increase in cybercrimes and the theft of personal information through social media, email, and phishing websites that seem like the websites that are frequently used to steal user data such a credit card number or login ID. Phishing, a common form of cybercrime, poses a risk to online security through the theft of personal information, and with the COVID-19 virus's appearance, which has drawn more people and businesses online and forced many of them to work remotely, there is now an increase in the number of phishing threats. Researchers have chosen Machine Learning (ML) as an effective technique for differentiating harmful software web sites from legitimate web pages based on past findings and study. The deep neural networks using MLP got the maximum performance among all techniques, according to monitoring results. This study presents a valuable contribution to the development of an intelligent system that can detect fake materials on the internet, ultimately enhancing online security and protecting users' personal data. |
| Keywords |
| Deep Neural Networks, MLP Classifier, Fake Websites, Machine learning, legitimate Websites. |
| Status: Abstract Accepted (Oral Presentation) |