Classifying and Identifying of Threats in E-mails - Using Data Mining Techniques
D. V. Chandra Shekar, S. Sagar Imambi · 2008
E-mail has become one of the most ubiquitous methods of communication. The large percentage of the total traffic over the internet is thee-mail. E-mail data is also growing rapidly, creating needs for automated analysis. So, to detect crime, to organize bundles of emails, a spectrum of techniques should be applied to discover and identify patterns and make predictions. Data mining has emerged to address problems of understanding ever-growing volumes of information for structured data, finding patterns within data that are used to develop useful knowledge. Earlier Statistical methods are used to characterize user behavior , classifying spam and detecting novel email viruses. However, previous techniques have not examined the contributions of these features to their classification and they need some improvements. By applying data mining classification techniques we can achieve this very efficiently. In this paper we show that Naive Bayes classification approach is useful for predicting user's behavior and to organize the emails according to users constraints.