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.

Keywords

• Cybersecurity
• Phishing Website Detection
• Metaheuristic Optimization
• Honey Badger Algorithm
• Artificial Neural Network