Abstract
Prevention is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanisms has motivated many security researchers and practitioners, who are studies threat trend analysis models. However, threat trend is not directly revealed from the time-series data because the trend is implicit in its nature. Besides, traditional time-series analysis, which predicts the future trend pattern by relying exclusively on the past trend pattern, is not appropriate for predicting a trend pattern in dynamic network environments (e.g., the Internet). Thus, supplemental environmental information is required to uncover a trend pattern from the implicit (or hidden) raw data. In this paper, we propose Cyber Threat Trend Analysis Model using Hidden Markov Model (HMM) by incorporating the supplemental environmental information into the trend analysis.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IAS 2007 3rd Internationl Symposium on Information Assurance and Security |
| Pages | 177-182 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | 3rd Internationl Symposium on Information Assurance and Security, IAS 2007 - Manchester, United Kingdom Duration: 2007 Aug 20 → 2007 Aug 31 |
Other
| Other | 3rd Internationl Symposium on Information Assurance and Security, IAS 2007 |
|---|---|
| Country | United Kingdom |
| City | Manchester |
| Period | 07/8/20 → 07/8/31 |
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ASJC Scopus subject areas
- Information Systems
- Information Systems and Management
Cite this
Cyber threat trend analysis model using HMM. / Kim, Do Hoon; Lee, Taek; Jung, Sung Oh David; In, Hoh; Lee, Heejo.
Proceedings - IAS 2007 3rd Internationl Symposium on Information Assurance and Security. 2007. p. 177-182 4299771.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cyber threat trend analysis model using HMM
AU - Kim, Do Hoon
AU - Lee, Taek
AU - Jung, Sung Oh David
AU - In, Hoh
AU - Lee, Heejo
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Prevention is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanisms has motivated many security researchers and practitioners, who are studies threat trend analysis models. However, threat trend is not directly revealed from the time-series data because the trend is implicit in its nature. Besides, traditional time-series analysis, which predicts the future trend pattern by relying exclusively on the past trend pattern, is not appropriate for predicting a trend pattern in dynamic network environments (e.g., the Internet). Thus, supplemental environmental information is required to uncover a trend pattern from the implicit (or hidden) raw data. In this paper, we propose Cyber Threat Trend Analysis Model using Hidden Markov Model (HMM) by incorporating the supplemental environmental information into the trend analysis.
AB - Prevention is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanisms has motivated many security researchers and practitioners, who are studies threat trend analysis models. However, threat trend is not directly revealed from the time-series data because the trend is implicit in its nature. Besides, traditional time-series analysis, which predicts the future trend pattern by relying exclusively on the past trend pattern, is not appropriate for predicting a trend pattern in dynamic network environments (e.g., the Internet). Thus, supplemental environmental information is required to uncover a trend pattern from the implicit (or hidden) raw data. In this paper, we propose Cyber Threat Trend Analysis Model using Hidden Markov Model (HMM) by incorporating the supplemental environmental information into the trend analysis.
UR - http://www.scopus.com/inward/record.url?scp=46749088696&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=46749088696&partnerID=8YFLogxK
U2 - 10.1109/IAS.2007.19
DO - 10.1109/IAS.2007.19
M3 - Conference contribution
AN - SCOPUS:46749088696
SN - 0769528767
SN - 9780769528762
SP - 177
EP - 182
BT - Proceedings - IAS 2007 3rd Internationl Symposium on Information Assurance and Security
ER -