Name
Abstract | ||
|---|---|---|
As adversary activities move into cyber domains, attacks are not necessarily associated with physical entities. As a result, observations of an enemy's course of action (eCoA) may be sporadic, or non-uniform, with potentially more missing and noisy data. Traditional classification methods, in this case, can become ineffective to differentiate correlated observations or attack tracks. This paper formalizes this new challenge and discusses three solution approaches from seemingly unrelated fields. This attempt sheds new light to the problem of classifying unknown types of non-uniform cyber attack tracks. |
Year | Venue | Keywords |
|---|---|---|
2009 | Fusion | fourier analysis,subsequence matching,enemy course of action,cyber fusion,non-uniform cyber attack tracks,social computing,physical entities,security of data,unsupervised classification,intrusion detection,trajectory,clustering algorithms,computer security,predictive models,interpolation,machine learning,frequency response,classification algorithms,servers |
Field | DocType | ISBN |
Noisy data,Computer science,Computer security,Server,Artificial intelligence,Social computing,Cluster analysis,Computer vision,Course of action,Cyber-attack,Adversary,Statistical classification,Machine learning | Conference | 978-0-9824-4380-4 |
Citations | PageRank | References |
1 | 0.37 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Du | 1 | 55 | 6.88 |
| Christopher Murphy | 2 | 30 | 4.28 |
| Jordan Bean | 3 | 1 | 0.37 |
| Shanchieh Jay Yang | 4 | 131 | 23.11 |