Abstract
The authors have proposed a method of identifying superspreaders by flow sampling and a method of extracting worm-infected hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters, φ, the measurement period length, m *, the identification threshold of the flow count m within φ, and H *, the identification probability for hosts with m∈=∈m *, remains unsolved. These three parameters seriously affect the worm-spreading property. In this paper, we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all the vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 103-116 |
| Number of pages | 14 |
| Volume | 5275 LNCS |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 8th IEEE International Workshop on IP Operations and Management, IPOM 2008 - Samos Island Duration: 2008 Sep 22 → 2008 Sep 26 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5275 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 8th IEEE International Workshop on IP Operations and Management, IPOM 2008 |
|---|---|
| City | Samos Island |
| Period | 08/9/22 → 08/9/26 |
Fingerprint
Keywords
- Detection
- Optimum design
- Sampling
- Worm
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Optimum identification of worm-infected hosts. / Kamiyama, Noriaki; Mori, Tatsuya; Kawahara, Ryoichi; Harada, Shigeaki.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5275 LNCS 2008. p. 103-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5275 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Optimum identification of worm-infected hosts
AU - Kamiyama, Noriaki
AU - Mori, Tatsuya
AU - Kawahara, Ryoichi
AU - Harada, Shigeaki
PY - 2008
Y1 - 2008
N2 - The authors have proposed a method of identifying superspreaders by flow sampling and a method of extracting worm-infected hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters, φ, the measurement period length, m *, the identification threshold of the flow count m within φ, and H *, the identification probability for hosts with m∈=∈m *, remains unsolved. These three parameters seriously affect the worm-spreading property. In this paper, we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all the vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine.
AB - The authors have proposed a method of identifying superspreaders by flow sampling and a method of extracting worm-infected hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters, φ, the measurement period length, m *, the identification threshold of the flow count m within φ, and H *, the identification probability for hosts with m∈=∈m *, remains unsolved. These three parameters seriously affect the worm-spreading property. In this paper, we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all the vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine.
KW - Detection
KW - Optimum design
KW - Sampling
KW - Worm
UR - http://www.scopus.com/inward/record.url?scp=56649111112&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56649111112&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-87357-0_9
DO - 10.1007/978-3-540-87357-0_9
M3 - Conference contribution
AN - SCOPUS:56649111112
SN - 3540873562
SN - 9783540873563
VL - 5275 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 103
EP - 116
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -