Cluster-Based Antiphishing (CAP) Model for Smart Phones

Table 1

Critical review of the literature schemes.

Approach	Classification diagram	Mechanism	Contribution	Limitations	Implementation scenario	Technology/algorithms used/tools
Network-level protection [14]	IP	Internet service providers database is used	Attack detection good, offender address list is up to date	Rule tuning, message content not verified	DNS	Snort
Authentication [15]	Certificates	User, domain, e-mail, and transaction based authentication based on digital signatures and hashing	Less complexity, no need of inter e-mail domains cooperation, enhance security	Vulnerable to man-in-the-middle attacks, technology constraints	Hotmail, Yahoo, Gmail	PGP, S/MIME
Client-side tools [16]	URL	Whitelisting and blacklisting	Legitimate e-mail will be acceptable only, best for already known phishing websites	High false positive and false negative rate for white- and blacklisting respectively	Mozilla, Firefox, and Internet Explorer browsers	Net craft, eBay toolbar, IE phishing filter
User education [17]	Social engineering	Online material, online test, and contextual training	Authority, attractive and impressive	False negative	All scenarios	Smart OS
Server-side filters and classifiers [18]	Technical maneuvers	Compare multiple classifiers and clustering techniques	Discover phishing attacks with narrow earlier knowledge	Time and space tradeoff	Internet browsers	Support vector machines