Abstract
Over the last few years, web phishing attacks have been constantly evolving causing customers to lose trust in e-commerce and online services. Various tools and systems based on a blacklist of phishing websites are applied to detect the phishing websites. Unfortunately, the fast evolution of technology has led to the born of more sophisticated methods when building websites to attract users. Thus, the latest and newly deployed phishing websites; for example, zero-day phishing websites, cannot be detected by using these blacklist-based approaches. Several recent research studies have been adopting machine learning techniques to identify phishing websites and utilizing them as an early alarm method to identify such threats. However, the important website features have been selected based on human experience or frequency analysis of website features in most of these approaches. In this paper, intelligent phishing website detection using particle swarm optimization-based feature weighting is proposed to enhance the detection of phishing websites. The proposed approach suggests utilizing particle swarm optimization (PSO) to weight various website features effectively to achieve higher accuracy when detecting phishing websites. In particular, the proposed PSO-based website feature weighting is used to differentiate between the various features in websites, based on how important they contribute towards recognizing the phishing from legitimate websites. The experimental results indicated that the proposed PSO-based feature weighting achieved outstanding improvements in terms of classification accuracy, true positive and negative rates, and false positive and negative rates of the machine learning models using only fewer websites features utilized in the detection of phishing websites.
Highlights
In recent years, the number of web users who use online services, online shopping, and e-banking has been increasing rapidly due to flexibility, comfort, and ease of use
The proposed method is based on the great performance of particle swarm optimization (PSO), which is one the most well-known evolutionary algorithms that are search for the best solution, but they are able to evolve solutions to produce the optimal solution. Unlike the former and known feature selection methods discussed in the literature, the proposed method employs PSO to weight the website features effectively in order to increase the performance of machine learning
DATASET COLLECTION In this paper, we conducted the experiments with the phishing websites dataset available for free use in UCI Machine Learning Repository [46] in order to evaluate the performance of the proposed PSO-based feature weighting approach suggested to improve the phishing website detection
Summary
The number of web users who use online services, online shopping, and e-banking has been increasing rapidly due to flexibility, comfort, and ease of use. This paper proposes implementing particle swarm optimization (PSO) to produce and assign a weight to each website feature in order to help in increasing the accuracy of phishing website detection with feasible computation and resources. This weight represents the importance and relevance of the feature for phishing website detection. Unlike the former and known feature selection methods discussed in the literature, the proposed method employs PSO to weight the website features effectively in order to increase the performance of machine learning.
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