6th European Conference - Academic Conferences

Manoj Cherukuri and Srinivas Mukkamala
Figure 7: Overview of the process for the construction of the dataset
Table 1: Top five countries by the number of malicious websites hosted in our dataset
5. Link analysis
Country Number of malicious websites
United States 14790
Philippines 1086
Canada 432
Germany 183
United Kingdom 143
5.1 Outdegree and indegree of malicious websites
The indegree and the outdegree of the malicious websites within the dataset were computed and
plotted two graphs representing the count of the malicious domains versus the indegree and the
outdegree. For computing the indegree and the outdegree of the websites, we considered only the
links among different domains as most of the links within the same domain were identified to be
navigational links. The count versus the indegree and the outdegree graphs are shown in Figure 8.
The outdegree and the indegree of the malicious websites did not satisfy the power law in contrast to
the World Wide Web graph (Watts and Strogatz, 1998).
In an attempt to identify an equation that suites the indegrees and oudegrees of malicious websites,
we identified that the malicious websites satisfy the power law with an exponential cutoff. The Lambda
and the Gamma values of the power law with exponential cutoff equation for the indegree and the
outdegree of malicious websites were identified to be 12.32, 0.9 and 8.32, 1.02 respectively.
Correlation coefficient was measured to verify the fit of these equations. The correlation coefficient
was 0.98 and 0.99 for the indegree and the outdegree respectively signifying a good fit.
.
Where
is the exponential cutoff and is the power law term (Clustering Coefficient, 2010)
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