Determination of Learning Architecture to Detect Int-phish Phishing Detection

Abstract

Phishing is a deceptive culture and a shape of a cyber-attack schematic which evolved with the sole intention of collecting confidential information by containing the camouflage of the original website. Most of the people lead a broad range of business online, they can offer and purchase merchandise, perform diverse banking deeds and indeed take part in political and social selection through online vote casting. Neither purchaser nor vendor needs to meet for any type of transaction and a purchaser can in some cases be trading with a deceptive business that does not really exist. An ordinary hazard comes from reputed phishing websites, which have become an issue for online banking and e-commerce clients. Phishing websites endeavor to trap individuals into uncovering secure data in order for the fraudster to get to their accounts. The websites that look like legitimate entities used for users who lack knowledge of browser clues and security indicators. The aim of the study is to propose an intelligent framework to detect phishing URLs which generates a scientific report by evaluating various multi-layer approaches. This scientific report provides information on the best architecture for phishing URLs detection and also helps antiphishing tools developers to make an initial decision about approach that should be followed. This paper proposed a novel phishing URLs detection architecture using a) Deep Neural Network (DNN) b) Neural Network (NN) c) Stacking. In the first level, stacking base classifier provides temporary prediction along with cross validation and crisps prediction. After the completion of the cross validation, the second level requires another additional classifier called meta-estimator that is used in the train set and performed on a test set for final prediction. Neural networks work well with this dataset for better training, time and complexity. Two types of neural networks are used for neural network architecture, five layers are used for deep neural networks and two layers are used for artificial neural networks. Optimized parameters have been used for neural network architecture, along with five types of adaptive learning optimization algorithms, in combination with which a better result is selected. In the case of five-layer Deep Neural networks along with 50 epochs can provide higher accuracy of 0.95, the minimum mean squared error of 0.30, and also a minimum error rate of 0.074. Using two-layer neural networks along with 150 epochs can provide higher accuracy of 0.95, the minimum mean squared error of 0.29 and also a minimum error rate of 0.07. Stack generalization can reach maximum accuracy 0.97 in binary classification and also provide minimum error rate MAE 2.1. Machine learning approaches were utilized to identify the modern as well as the variation of malicious URL viably. In any case, by the advancement of exploration in machine learning-based inquiry about, it can be observed that deep learning-based architectures performed better in comparison to the machine learning algorithm.