Name
Abstract | ||
|---|---|---|
Classifying host roles based on network traffic behavior is valuable for network security analysis and detecting security policy violation. Behavior-based network security analysis has advantages over traditional approaches such as code patterns or signatures. Modeling host roles based on network flow data is challenging because of the huge volume of network traffic and overlap among host roles. Many studies of network traffic classification have focused on classifying applications such as web, peer-to-peer, and DNS traffic. In general, machine learning approaches have been applied on classifying applications, security awareness, and anomaly detection. In this paper, we present a supervised machine learning approach that use On-Line Support Vector Machine and Decision Tree to classify host roles. We collect sFlow data from main gateways of a large campus network. We classify different roles, namely, clients versus servers, regular web non-email servers versus web email servers, clients at personal offices versus public places of laboratories and libraries, and personal office clients from two different colleges. We achieved very high classification accuracy, i.e., 99.2% accuracy in classifying clients versus servers, 100% accuracy in classifying regular web non-email servers versus web email servers, 93.3% accuracy in classifying clients at personnel offices versus public places, and 93.3% accuracy in classifying clients at personal offices from two different colleges. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2465839.2465847 | HPPN@HPDC |
Keywords | Field | DocType |
classifying application,network flow data,host role,large campus network,supervised machine,regular web non-email server,classifying client,web email server,behavior-based network security analysis,different college,personal office,decision tree,network security,anomaly detection,network flow,security policy,machine learning,support vector machine | Traffic classification,sFlow,Security awareness,Decision tree,World Wide Web,Computer science,Campus network,Server,Network security,Artificial intelligence,Security policy,Machine learning | Conference |
Citations | PageRank | References |
8 | 0.69 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingdong Li | 1 | 14 | 1.83 |
| Mehmet Hadi Günes | 2 | 149 | 16.19 |
| George Bebis | 3 | 2397 | 149.44 |
| Jeff Springer | 4 | 8 | 1.36 |