Name
Abstract | ||
|---|---|---|
Nowadays, worms and other outside threats in the network recognized to be a serious and unexpected behavior. The main issue was addressed based on the behavioral patterns of worms that reflect application communications typical of worms. This representation of worm's behavior differs from those used in contemporary enterprise postures, which reliance on a particular type of signature-based intrusion detection and the behavioral detection approach contrasts from this form of signature-based detection. Thus, this paper introduced the traditional worm's detection approaches. Meanwhile, the paper suggested a worm detection approach based on worm behaviors that consist on network scanning detection approach, network worm's correlation approach, and signature correlation approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/UKSIM.2011.57 | UKSim |
Keywords | Field | DocType |
signature correlation approach,behavioral detection approach,investigation towards worms detection,signature-based intrusion detection,traditional worm,worm detection approach,worm behavior,signature-based detection,correlation approach,network worm,detection approach,artificial neural network,accuracy,ids,internet,artificial neural networks,algorithm design,algorithm design and analysis,computer network security,correlation | Behavioral pattern,Data mining,Signature detection,Algorithm design,Computer science,Network security,Artificial neural network,Grippers,Intrusion detection system,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Anbar | 1 | 16 | 9.05 |
| Ahmed M. Manasrah | 2 | 9 | 4.36 |