An Intelligent Prediction of Phishing URLs Using ML Algorithms

LEARNING TO DETECT PHISHING URLs

Phishing attacks have been on the rise and performing certain actions such as mouse hovering, clicking, etc. on malicious URLs may cause unsuspecting Internet users to fall victims of identity theft or other scams. In this paper, we study the anatomy of phishing URLs that are created with the specific intent of impersonating a trusted third party to trick users into divulging personal data. Unl...

Lightweight Phishing URLs Detection Using N-gram Features

Phishing is a kind of attack that belongs to social engineering and this attack seeks to trick people in order to let them reveal their confidential information. Several methods are introduced to detect phishing websites by using different types of features. Unfortunately, these techniques implemented for specific attack vector such as detecting phishing emails which make implementing wide scop...

Hybrid Intelligent Approach in Prediction of Phishing Attacks

Diagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms

Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tu...