A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection

Fuzzy system is one of the most used systems in the decision-making and classification method as it is easy to understand because the way this system works is closer to how humans think. It is a system that uses human experts to hold the membership values to make decisions. However, it is hard to determine the fuzzy parameter manually in a complex problem, and the process of generating the parameter is called fuzzy modelling. Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. The advantages of both algorithms are used to balance the exploration and exploitation in the searching process. Two datasets from UCI machine learning were used: Website Phishing Dataset and Phishing Websites Dataset. As a result, the average accuracy for WPD and PWD was 98.69% and 98.80%, respectively. In conclusion, the proposed method shows promising and effective results compared to other methods.

Automated summary

This paper investigates the optimization problem of fuzzy systems and fuzzy modeling, and improves the Butterfly Optimization Algorithm by combining it with Harmony Search. The proposed method provides a way to achieve optimal results in fuzzy modeling. Experimental results show that the method achieves very high accuracy on two datasets.