A PCA Analysis of Daily Unwanted Traffic

This paper investigates the macroscopic behavior of unwanted traffic (e.g., virus, worm, backscatter of (D)DoS or misconfiguration) passing through the Internet. The data set we used are unwanted packets measured at /18 darknet in Japan from Oct. 2006 to Apr. 2009 that included the recent Conficker outbreak. The traffic behavior is quantified by the entropy of ten packet features (e.g., 5-tuple). Then, we apply PCA (principal component analysis) to a ten dimensional entropy time series matrix to obtain a suitable representation of unwanted traffic. PCA is a well-known and studied method for finding out normal and anomalous behaviors in Internet backbone traffic, however, few studies applied it to darknet traffic. We first demonstrate the high variability nature of the entropy time series for ten packet features. Next, we show that the top four principal components are sufficiently enough to describe the original traffic behavior. In particular, the first component can be interpreted as the type of unwanted traffic (i. e., worm/virus or scanning), and the second one as the difference in communication patterns (e. g., one-to-many or many-to-one). Those two components account for 63.8\% of the original data set in terms of the total variance. On the other hand, the outliers in the higher components indicate the presence of specific anomalies although most of mapped data to the components have less variability. Furthermore, we show that the scatter plot of the first and second principal component scores provides us with a better view of the macroscopic unwanted traffic behavior.