MASPHID: A Model to Assist Screen Reader Users for Detecting Phishing Sites Using Aural and Visual Similarity Measures

Phishing is one of the major issues in cyber security. In phishing, attackers steal sensitive information from users by impersonation of legitimate websites. This information captured by phisher is used for variety of scenarios such as buying goods using online transaction illegally or sometime may sell the collected user data to illegal sources. Till date, various detection techniques are proposed by different researchers but still phishing detection remains a challenging problem. While phishing remains to be a threat for all users, persons with visual impairments fall under the soft target category, as they primarily depend on the non-visual web access mode. The persons with visual impairments solely depends on the audio generated by the screen readers to identify and comprehend a web page. This weak-link shall be harnessed by attackers in creating impersonate sites that produces same audio output but are visually different. This paper proposes a model titled "MASPHID" (Model for Assisting Screenreader users to Phishing Detection) to assist persons with visual impairments in detecting phishing sites which are aurally similar but visually dissimilar. The proposed technique is designed in such a manner that phishing detection shall be carried out without burdening the users with technical details. This model works against zeroday phishing attack and evaluate high accuracy.