Dynamic spam detection system and most relevant features identiﬁcation using random weight network
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Nowadays e-mail is being used by millions of people as an eﬀective form of formal or informal communication over the Internet and with this high-speed form of communication there comes a more eﬀective form of threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. By these unsolicited emails, the Internet users are hugely impacted in terms of security concerns as well as being exposed to contents that are not appropriate for certain users. There is no way to stop spammers using static ﬁlters because almost every other day they ﬁnd a new way to bypass the ﬁlter. New techniques are introduced to elude this system. In this paper, a smart and dynamic(adaptive) system is proposed that will be using Random Weight Network (RWN) to approach spam in a diﬀerent way and meanwhile this will also detect the most relevant features that will help to design the spam ﬁlter. A spam ﬁlter with the capability of identifying spam automatically will also be embedded in the proposed system. Also a comparison of diﬀerent parameters for diﬀerent RWN models have been shown to determine which model works best with what parameters under diﬀerent situations.