Name
Abstract | ||
|---|---|---|
Detection of sophisticated network scans, such as low and slow scans, requires correlation of large amounts of network data over long periods of time. The volume of data obfuscating such scans can be overwhelming and makes computation challenging. Such scans pose network security risks since identifying running services, the goal of executing such scans, is the first step in launching an attack on the scanned host. To detect sophisticated scans we propose the integration of graph feature extraction techniques with visualization to simultaneously optimize computational complexity and human analyst time. The integrated approach uses graph modeling and preprocessing to make visual displays easy to comprehend, and uses human intervention to avoid solving NP-hard computational problems while still providing real-time visualization. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2459976.2460010 | CSIIRW |
Keywords | Field | DocType |
optimize computational complexity,sophisticated network,human analyst time,np-hard computational problem,fast port,real-time visualization,network data,human intervention,graph modeling,network security risk,visualizing graph feature,graph feature extraction technique,system security,formal verification,model checking | Data mining,Model checking,Computer security,Computer science,Artificial intelligence,Computer vision,Computational problem,Visualization,Network security,Feature extraction,Preprocessor,Formal verification,Computational complexity theory | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maggie Cheng | 1 | 11 | 2.06 |
| Quanmin Ye | 2 | 14 | 2.58 |
| Robert F. Erbacher | 3 | 202 | 27.65 |