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Figure 1 Example scoring for in-degree method.
While doing this, the in-degree method maintains a very low false positive rate.
Our results for the in-degree method alone are summarized in Table 3.
Removing these scans before applying the in-degree method significantly enhanced the ability of the in-degree method to detect the remaining scanners; it did not improve the detection rate of the TRW algorithm.
We combined in-degree method with TRW and simple rate limiting to form a highly effective ensemble algorithm.
We furthermore show how pre-filtering our in-degree method with a rate-based scanning method can substantially reduce the false positive rate.
We now explore how the in-degree method complements TRW detection and how rate-based detection methods can reduce in-degree false negatives.
This feature allows the in-degree method to function in situations where asymmetric routing exists without any need for modification (c.f. [7], where the TRW approach required modification to function adequately under asymmetric routing conditions).
The in-degree method is shown to detect scans not identified by the TRW method, and to do so with increased accuracy when the data is pre-processed to remove scans detected by a simple rate-limiting method.
While the TRW method did successfully identify the majority of simulated scans as the number of probes per IP address approached 10, the success rate was markedly less than that of the in-degree method.
In 6 cases these obvious scans lowered the S scores of other scanners (for the unfiltered in-degree instance), interfering with the ability of the in-degree method to detect them.
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