Hybrid Honey Badger Algorithm with Artificial Neural Network (HBA-ANN) for Website Phishing Detection

Abstract

 Phishing is a sort of cyberattack that refers to the practice of fabricating fake websites that imitate
authentic websites in order to trick users into disclosing private information. Identifying these fake sites is
challenging due to their deceptive nature as they often mimic legitimate websites, making it difficult for users to
distinguish between the real and fake ones. Artificial Neural Network (ANN) is one popular method for website
phishing detection. ANN is capable of detecting phishing websites by identifying patterns and characteristics
connected to phishing websites through a network training phase. Technically, in the network training phase of
ANN, neurons on the network must be passed over. There are multiple techniques in training the network, one of
which is training with metaheuristic algorithms. Metaheuristic algorithms that aim to develop more effective
hybrid algorithms by combining the good and successful aspects of more than one algorithm are algorithms
inspired by nature. Therefore, this study proposed a hybrid Honey Badger Algorithm with Artificial Neural
Network (HBA-ANN) classification model. HBA as metahueristic algorithm is used to optimize the network
training process of ANN to improve their performances. Three main steps made up the proposed HBA-ANN
classification model: setting up the experiment, optimizing HBA for network training, and network testing. Lastly,
the performance of the proposed HBA-ANN classification model is assessed in terms of recall, precision, F1-score,
accuracy and error rate using the confusion matrix that was generated for analysis. The proposed hybrid HBAANN was found to be effective in identifying the phishing website after conducting an experimental and statistical
analysis.