Name
Abstract | ||
|---|---|---|
Using probabilistic learning, we develop a naive Bayesian classifier to passively infer a host's operating system from packet headers. We analyze traffic captured from an Internet exchange point and compare our classifier to rule-based inference tools. While the host operating system distribution is heavily skewed, we find operating systems that constitute a small fraction of the host count contribute a majority of total traffic. Finally as an application of our classifier, we count the number of hosts masquerading behind NAT devices and evaluate our results against prior techniques. We find a host count inflation factor due to NAT of approximately 9% in our traces. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-24668-8_16 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
operating system,rule based | Data mining,Internet Protocol,Internet exchange point,Computer science,Inference,Network packet,Internet protocol suite,Real-time computing,Transmission Control Protocol,Artificial intelligence,Probabilistic logic,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
3015 | 0302-9743 | 49 |
PageRank | References | Authors |
2.71 | 12 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Beverly | 1 | 361 | 32.92 |