Mobile agent based intrusion detection system adopting Hidden Markov Model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Do Hyeon | - |
dc.contributor.author | Kim, Doo Young | - |
dc.contributor.author | Jung, Jaeil | - |
dc.date.accessioned | 2022-12-21T06:54:48Z | - |
dc.date.available | 2022-12-21T06:54:48Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2007-08 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179746 | - |
dc.description.abstract | Mobile agent based intrusion detection systems distribute detection agents to prevent system from shutting down when an agent becomes breaks. Because in this system agents are distributed, it can reduce network delay and network load, and each agent can operate independently. Agents can also be easily added or deleted. In this paper we propose an enhanced design of mobile agent based intrusion detection system using Hidden Markov Model algorithm for detection. Hidden Markov Model algorithm is used to detect abnormal behavior pattern by analyzing log information. By adopting this algorithm to the mobile intrusion detection agents, detection performance can be improved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Mobile agent based intrusion detection system adopting Hidden Markov Model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Jaeil | - |
dc.identifier.doi | 10.1007/978-3-540-74477-1_12 | - |
dc.identifier.scopusid | 2-s2.0-38049100326 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4706 LNCS, no.PART 2, pp.122 - 130 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 4706 LNCS | - |
dc.citation.number | PART 2 | - |
dc.citation.startPage | 122 | - |
dc.citation.endPage | 130 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Delay control systems | - |
dc.subject.keywordPlus | Hidden Markov models | - |
dc.subject.keywordPlus | Information analysis | - |
dc.subject.keywordPlus | Mobile agents | - |
dc.subject.keywordPlus | Detection agents | - |
dc.subject.keywordPlus | Intrusion detection systems | - |
dc.subject.keywordPlus | Network loads | - |
dc.subject.keywordPlus | System agents | - |
dc.subject.keywordPlus | Intrusion detection | - |
dc.subject.keywordAuthor | Hidden Markov Model | - |
dc.subject.keywordAuthor | Intrusion detection | - |
dc.subject.keywordAuthor | Mobile agent | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-74477-1_12 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.