Learning from Network Traffic: Computing Kernels over Connection Content
author:
Pavel Laskov,
Fraunhofer FIRST
Categories
Top: Computer Science: Network AnalysisTop: Computer Science: Machine Learning: Kernel Methods
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| Slides | |
| 0:00 | Learning from network traffic |
| 0:06 | Motivation |
| 1:23 | Intrusion detection |
| 5:13 | Features used in intrusion |
| 7:02 | Byte histograms |
| 8:08 | Better ways to handle network |
| 8:54 | Why n-grams are better |
| 10:06 | Similarity measures over n-grams |
| 10:51 | Trie representation |
| 12:06 | Trie matching algorithm |
| 12:43 | Trie matching algorithm1 |
| 12:51 | Trie matching algorithm2 |
| 12:56 | Trie matching algorithm3 |
| 13:03 | Trie matching algorithm4 |
| 13:04 | Trie matching algorithm5 |
| 13:05 | Trie matching algorithm6 |
| 13:05 | Trie matching algorithm7 |
| 13:30 | Trie matching algorithm8 |
| 13:33 | Trie matching algorithm9 |
| 13:57 | Similarity computation using tries |
| 15:08 | Syntax trees |
| 15:50 | String kernel computation |
| 17:06 | Results |
| 18:38 | Conclusions |
| 19:19 | TITLE |
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