Name
Abstract | ||
|---|---|---|
•A novel approach for P2P botnet detection using ‘signal-processing’ approaches of Fourier transforms and information entropy.•Detection of stealthy P2P botnets in presence of traffic from benign P2P applications.•Detection models were evaluated for their robustness by injecting noise in the ‘testing’ dataset. Our approach gave higher True Positive rate (90%) as compared to results obtained with features used by past research. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.comcom.2016.05.017 | Computer Communications |
Keywords | Field | DocType |
Botnet,Machine learning,Peer-to-peer,Intrusion detection,Security | Peer-to-peer,Computer science,Computer security,Botnet,Computer network,Artificial intelligence,Adversary,Entropy (information theory),True positive rate,Intrusion detection system,Machine learning | Journal |
Volume | ISSN | Citations |
96 | 0140-3664 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pratik Narang | 1 | 60 | 11.31 |
| Chittaranjan Hota | 2 | 129 | 16.89 |
| Husrev Taha Sencar | 3 | 1 | 0.35 |