MMSPhiD: a phoneme based phishing verification model for persons with visual impairments

This paper aims to propose a model entitled MMSPhiD (multidimensional similarity metrics model for screen reader user to phishing detection) that amalgamates multiple approaches to detect phishing URLs.

The model consists of three major components: machine learning-based approach, typosquatting-based approach and phoneme-based approach. The major objectives of the proposed model are detecting phishing URL, typosquatting and phoneme-based domain and suggesting the legitimate domain which is targeted by attackers.

The result of the experiment shows that the MMSPhiD model can successfully detect phishing with 99.03 per cent accuracy. In addition, this paper has analyzed 20 leading domains from Alexa and identified 1,861 registered typosquatting and 543 phoneme-based domains.

The proposed model has used machine learning with the list-based approach. Building and maintaining the list shall be a limitation.

The results of the experiments demonstrate that the model achieved higher performance due to the incorporation of multi-dimensional filters.

In addition, this paper has incorporated the accessibility needs of persons with visual impairments and provides an accessible anti-phishing approach.

This paper assists persons with visual impairments on detection phoneme-based phishing domains.