Abstract
Darknet monitoring is very important for understanding various botnet activities for early detection and defense the threats on the Internet caused by the botnets. However, common illegal accesses by ordinary malware make such detection difficult. To remove such accesses by ordinary malware from the results of network monitoring, we propose a data screening method based on finding frequent sequential patterns that appear in given traffic data. We applied our method to traffic data observed in the darknet and report the results.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Control and Innovative Computing |
| Pages | 363-375 |
| Number of pages | 13 |
| DOIs | |
| Publication status | Published - Jan 16 2012 |
| Event | International Conference on Advances in Intelligent Control and Innovative Computing - Hong Kong, Hong Kong Duration: Mar 16 2011 → Mar 18 2011 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 110 LNEE |
| ISSN (Print) | 1876-1100 |
| ISSN (Electronic) | 1876-1119 |
Other
| Other | International Conference on Advances in Intelligent Control and Innovative Computing |
|---|---|
| Country | Hong Kong |
| City | Hong Kong |
| Period | 3/16/11 → 3/18/11 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
Cite this
Network traffic screening using frequent sequential patterns. / Tsuruta, Hisashi; Shoudai, Takayoshi; Takeuchi, Jun'ichi.
Intelligent Control and Innovative Computing. 2012. p. 363-375 (Lecture Notes in Electrical Engineering; Vol. 110 LNEE).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Network traffic screening using frequent sequential patterns
AU - Tsuruta, Hisashi
AU - Shoudai, Takayoshi
AU - Takeuchi, Jun'ichi
PY - 2012/1/16
Y1 - 2012/1/16
N2 - Darknet monitoring is very important for understanding various botnet activities for early detection and defense the threats on the Internet caused by the botnets. However, common illegal accesses by ordinary malware make such detection difficult. To remove such accesses by ordinary malware from the results of network monitoring, we propose a data screening method based on finding frequent sequential patterns that appear in given traffic data. We applied our method to traffic data observed in the darknet and report the results.
AB - Darknet monitoring is very important for understanding various botnet activities for early detection and defense the threats on the Internet caused by the botnets. However, common illegal accesses by ordinary malware make such detection difficult. To remove such accesses by ordinary malware from the results of network monitoring, we propose a data screening method based on finding frequent sequential patterns that appear in given traffic data. We applied our method to traffic data observed in the darknet and report the results.
UR - http://www.scopus.com/inward/record.url?scp=84855646153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84855646153&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-1695-1_28
DO - 10.1007/978-1-4614-1695-1_28
M3 - Conference contribution
AN - SCOPUS:84855646153
SN - 9781461416944
T3 - Lecture Notes in Electrical Engineering
SP - 363
EP - 375
BT - Intelligent Control and Innovative Computing
ER -