Abstract
Phishing is a type of Internet fraud that aims to acquire the credential of users via scamming websites. In this paper, a novel approach is utilized that uses a Neural Network with a multilayer perceptron to detect the scam URL. The proposed system improves the accuracy of the scam detection system as it achieves a high accuracy percentage of 98.5%.
Highlights
In recent years, cyber-attacks are becoming increasingly common
The first category is the syntactic attack that are grouped under the name "malicious software" or "malware" and this type of attacks include viruses, Trojan horses, and worms
Phishing protection methods are classified into two main categories; denunciation platforms and heuristics-based solutions. denunciation platforms are built by developers and periodically provide the web browser with the updated blacklist [9]
Summary
Cyber-attacks are becoming increasingly common. The attackers use the computer as a tool or as a target and sometimes both. A cyber-attack deliberately destroys computers, steals information, or use a compromised computer as a starting point for other threats [1, 2]. The second category is semantic attacks, where the attackers collect the victim information through some websites or links that looks like trusted websites or to acquire his/her username, password, and credit card information [4]. The attacker uses Email, Website, URL to crack usernames, passwords and credit card details directly from users. Phishing emails are designed to sound as if they were sent from a lawful corporation or a recognized individual. Such emails aim to get the victim to visit a website that leads the victim to a fake website that claims to be legitimate.
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