Spam detection using a neural network classifier
David Ndumiyana, Munyaradzi Magomelo, Lucy Charity Sakala · 2013
The internet has undoubtedly become the linking tool for bringing together customers and business people, countries and regions, continents and islands regardless of their economic, political, cultural and social affiliations. Email service providers are going ahead on making email easy to use, allowing a variety of information to be conveniently and reliably sent through the Internet. The popularity of email has also brought with it challenges for Internet users and Internet Service Providers to the extent that if spamming problem is not dealt with urgently, benefits currently enjoyed by stakeholders would be surpassed by spam concerns. Although spam filtering techniques are now available on the market today, no one can deny that these solutions cannot guarantee 100% effectiveness at eliminating the problems of spam because a variety of these filters have weaknesses and strengths. This paper presents an alternative solution using a neural network classifier on a corpus of email messages received by the three researchers who conducted this investigation. The dataset for our system used descriptive attributes of words, symbols and email messages that are commonly used by email users to correctly identify spam received in email inboxes. The results show that our neural network classifier is able to detect and filter spam with success just like the others already on the market today.