| Hi-index | 0.00 |
We often receive unwanted information from a variety of electronic systems mainly through emails, electronic boards and messengers, called spam. Spam is the use of electronic messaging systems to send unsolicited bulk messages indiscriminately. Widely varying estimates of the cost associated with spam are available in the literature. However, a stochastic and quantitative analysis of the determinant characteristics of spam traffic is still an open problem. This work fills this gap. A 4-year data sample of real-time inbound traffic between May 2005 and July 2009 was collected to investigate and analyze characteristics of spam traffic through JIRANSOFT's Spam Sniper on the network at Korean Bible University. Our major findings of a statistical analysis of spam traffic are that (i) real-time inbound spam traffic is statistically more correlated (self-similar) when compared to normal traffic, and (ii) the degree of self-similarity measured in terms of the Hurst parameter H and obtained from different estimation techniques is very high.