Table 2: Summary of human and bot entropy values.
The decision threshold between human and bot players is set
as the mid-point of the average entropy values of human and bot users;
i.e., we choose (9.33+8.56)/2=8.95
as the threshold.
Table 3: Summary of data and results.
There are 138 traces, each of 10,000 seconds.
We use 80% of them for training and
the remainder for testing.
Table 5: The results based on six different detection schemes,
given the step-size inputs,
i.e., no temporal information
is considered. The table shows the average results of the
false positive rates, false negative rates and error rates,
after 10 repeats of the 10-fold cross-validation procedure.
Overall, the performance is enhanced if Isomap is applied.
The only exception is the poor performance
when applying linear SSVM in a low-dimensional space.
The decision boundary tends to become nonlinear
in a low-dimensional space;
thus, linear SSVM may not be appropriate in this case.
Figure 9: The histograms of the step sizes for trajectories
of (a) bot users and (b) human users.
A large number of constant size steps are found
in bot trajectories, but not in human trajectories.
However, after adding some Gaussian noise in (a) to (a1),
we obtain a histogram that is hard to distinguish, at least visually,
from that for a human user.
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