Name
Abstract | ||
|---|---|---|
Difficult to be detected in complex network environments, botnets have been huge threats to network security. As the circumscriptions of normal traffics and botnet traffics are blurring, the commonly used botnet detection methods based on traffic analysis often result in high false positive rates. To overcome this issue, we propose an effective botnet detection method based on fuzzy association rules. The proposed method can calculate the features of botnet traffic accurately, which can be used to recognize the normal traffic and botnet. We first collect the data in the laboratory by setting different botnets in the controlled experiment. The botnet traffic features, association rules support, trust and membership are calculated by the proposed method, which are further used to distinguish the type of botnet. When our method is compared with other methods in our data set, we find the former performs better. For the generality, we also test our method on the public data set and also find the higher accuracy rates, which demonstrates the proposed method is effective in detecting the botnets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8546312 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | Field | DocType |
Network security, botnet detection, pattern recognition, fuzzy association | Data mining,Traffic analysis,Pattern recognition,Botnet,Computer science,Server,Network security,Feature extraction,Association rule learning,Complex network,Artificial intelligence,Generality | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiazhong Lu | 1 | 4 | 2.81 |
| Fengmao Lv | 2 | 27 | 3.49 |
| Quanhui Liu | 3 | 24 | 3.16 |
| Malu Zhang | 4 | 30 | 8.19 |
| Xiaosong Zhang | 5 | 91 | 14.00 |