Phishing Detection Using Neural Network

The goal of this project is to apply multilayer feedforward neural networks to phishing email detection and evaluate the effectiveness of this approach. We design the feature set, process the phishing dataset, and implement the neural network (NN) systems. We then use cross validation to evaluate the performance of NNs with different numbers of hidden units and activation functions. We also compare the performance of NNs with other major machine learning algorithms. From the statistical analysis, we conclude that NNs with an appropriate number of hidden units can achieve satisfactory accuracy even when the training examples are scarce. Moreover, our feature selection is effective in capturing the characteristics of phishing emails, as most machine learning algorithms can yield reasonable results with it.