MODEL OF EMAIL MESSAGES IN THE PROBLEM OF DETECTING SPAM
Introduction. Currently, one of the popular information delivery methods and an important communication means is email message that exhibits numerous advantages like accessibility, efficiency and low cost, and can convey large amounts of data of various types. One of the typical business risks associated with emails is spam. Existing spam detection algorithms are typically based on the proven basic statistical processing and do not completely take into account the content of emails. The content is often changes depending on the tasks performed by users and changing information needs. The aim of the research was to develop an email message model that is suitable for automatic classification processes and allows to detect spam messages by their distinguishing features in terms of content. To build the model we employed a genetic approach of forming mathematical model of texts. That approach was verified to solve various problems. The key feature of the model is that it operates with the obtained from the original signal data converted from a numerical vector, quantized by level and discretized in time. This approach allows one to find the boundaries of sections of symbol sequences in texts, i.e. "Genes" of email messages. Findings. We proposed a genetic model of email messages that allows to select text segments of the message, that exhibit distinguishing features of spam. The key feature of the model is that it operates with data obtained from the source texts of email messages and converted to a numerical vector. This approach allows one to find the boundaries of sections of symbol sequences in texts, i.e. "Genes" of email messages, and make the spam detection process individual for the recipient. The proposed model was tested on 5019 email messages, consisiting of 3618 legal and 1401 spam ones. Research findings showed that the simplest decision rule, based on the principle of the majority of “genes” of the corresponding class, exhibits 80-90% detection efficiency.