Abstract

Phishing attacks appear in different forms other than forged emails and spoofed Web sites. They can exploit vulnerabilities in open wireless access points, Bluetooth, and handheld devices. Further, such attacks can be carried out using SMS or VoIP. In a mobile environment, such attacks are easier to set up and more convincing than traditional mass mailing techniques. Although traditional phishing attacks rely on fooling the recipient, in a mobile environment, the attack can take advantage of the limited (or lack of) security in mobile devices. Several ubiquitous solutions available for desktop and wired computers are generally not as readily available across wireless and mobile devices. This is due to the limitations in mobile devices, namely power, processing, and storage. Implementing traditional antiphishing solutions, such as machine learning approaches, in a mobile environment is inapplicable since some of these solutions are heavy in nature. Antiphishing solutions in a mobile environment should take advantage of the high predictive accuracy of machine learning approaches and at the same time conceal the high overhead associated with such approaches by building a distributed client-server framework to thwart the attacks.

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