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While effective, their approach requires coordination with DNS authorities in order to perform the redirection. Other studies used botnet infiltration to directly count the number of bot ID’s observed on the botnet command and control channel [17,31]. Unfortunately, botmasters are increasingly suppressing bot information on the command and control channel, thereby hindering the effectiveness of these techniques. As a remedy, we proposed [31] a technique for estimating botnet sizes using DNS cache probing.

We define to be our level of confidence of the test. If P(Xi, j ≥ Ci, j |X j = C j ) < , then (according to this test) there is a probability < that fi, j is healthy. By comparing this value to the observed values of the counters, we can determine, within a certain confidence level, whether one flow’s rate is higher with respect to the all the other flows’ rate. In the previous calculations, we don’t need to assume anything about the flows’ distribution. While any slight deviation from the normal transmission rate could be flagged as a violation, we allow a small degree of variability.

Within 2 standard deviations) of the mean estimation error remains within the bounds of the acceptable error set in the simulation. 1 In the Wild Evaluation We further validate the effectiveness of our estimation technique by applying it to data collected from a real-world DNS probing experiment. In this experiment, we Peeking Through the Cloud: DNS-Based Estimation 27 Table 1. Simulation parameters Acceptable estimation error (e) 20% Actual number of clients 100 Confidence 95% Number of samples (R) 100 100 80 Relative error (%) 60 40 20 0 −20 −40 −60 −80 −100 0 50 100 150 200 Number of DNS queries per TTL (λ TTL) Fig.

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