Phishing URL Detection: A Real-Case Scenario Through Login URLs
Product Description
Aim:
To provide an automated system for recognition the real-case Scenario through login URLs
Abstract:
Phishing is a social engineering cyber attack where criminals deceive users to obtain their credentials through a login form that submits the data to a malicious server. In this paper, we compare machine learning and deep learning techniques to present a method capable of detecting phishing websites through URL analysis. In most current state-of-the-art solutions dealing with phishing detection, the legitimate class is made up of homepages without including login forms. On the contrary, we use URLs from the login page in both classes because we consider it is much more representative of a real case scenario and we demonstrate that existing techniques obtain a high false-positive rate when tested with URLs from legitimate login pages. Additionally, we use datasets from different years to show how models decrease their accuracy over time by training a base model with old datasets and testing it with recent URLs. Also, we perform a frequency analysis over current phishing domains to identify different techniques carried out by phishers in their campaigns.
Problem definition:
In this traditional method the accuracy is low so our model will cannot predict the phishing URL accurately.that is very huge drawback in our existing system
Proposed System:
For the machine learning experiments, we empirically assign the parameters that returned the best accuracy on the phishing datasets. We used the averaged values of 10-fold cross-validation, reporting the accuracy, the precision and the recall denotes the true positives, i how many phishing websites were correctly classified. FP refers to the false positives and represents the number of legitimate samples wrongly classified as phishing. TN the true negatives) denotes the number of legitimate samples correctly classified. Finally, FN represents the false negatives that represent the number of phishing websites misclassified as legitimate ones.
Advantages:
Implemented a lexical feature selection from URL to optimize the number of features and the accuracy of their model. removed the less significant ones until they reached an optimal performance.
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