Recognizing and Handling the Malware Propagation in Large Scale Networks
| dc.contributor.author | Shishir, Umesh | |
| dc.contributor.author | Recognizing and Handling the Malware Propagation in Large Scale, Networks | |
| dc.contributor.author | Shobhita, G H | |
| dc.date.accessioned | 2017-08-18T12:12:52Z | |
| dc.date.available | 2017-08-18T12:12:52Z | |
| dc.date.issued | 2017-08-18T12:12:52Z | |
| dc.description.abstract | Malware is pervasive in networks, and poses a critical threat to network security. However, many people have very limited understanding of malware behavior in networks to date. The aim is to investigate how malware propagates in networks from a global perspective. The problem is formulated, and a rigorous two layer epidemic model for malware propagation from network to network is established. Based on the proposed model, the analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm theoretical findings. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/8521 | |
| dc.language.iso | en | en_US |
| dc.subject | Shobhita G H | en_US |
| dc.subject | Recognizing and Handling the Malware Propagation in Large Scale Networks | en_US |
| dc.subject | 1NH13IS087 | en_US |
| dc.subject | Shishir Umesh | en_US |
| dc.title | Recognizing and Handling the Malware Propagation in Large Scale Networks | en_US |
| dc.type | Other | en_US |