| dc.contributor.advisor | Thomas, Johnson | |
| dc.contributor.author | Koskei, Jordan Kiprop | |
| dc.date.accessioned | 2014-04-15T18:31:19Z | |
| dc.date.available | 2014-04-15T18:31:19Z | |
| dc.date.issued | 2011-07-01 | |
| dc.identifier.uri | https://hdl.handle.net/11244/8184 | |
| dc.description.abstract | Currently deployed intrusion detection systems (IDS) have no capacity to discover attacker high level intentions. Understanding an intruder's intention greatly enhances network security as it allows deployment of more accurate pre-emptive counter-measures and better disaster recovery. In this thesis, we propose a system where we model a known attack scenario using HMM and use alerts from an IDS later to discover an attackers set of intentions for a given set of alerts. | |
| dc.format | application/pdf | |
| dc.language | en_US | |
| dc.publisher | Oklahoma State University | |
| dc.rights | Copyright is held by the author who has granted the Oklahoma State University Library the non-exclusive right to share this material in its institutional repository. Contact Digital Library Services at lib-dls@okstate.edu or 405-744-9161 for the permission policy on the use, reproduction or distribution of this material. | |
| dc.title | Attacker Intention Discovery Layer for Intrusion Detection Systems Using Hidden Markov Models | |
| dc.type | text | |
| dc.contributor.committeeMember | Kak, Subhash C. | |
| dc.contributor.committeeMember | Toulouse, Michel | |
| osu.filename | Koskei_okstate_0664M_11496.pdf | |
| osu.college | Arts and Sciences | |
| osu.accesstype | Open Access | |
| dc.description.department | Computer Science Department | |
| dc.type.genre | Thesis | |
| dc.subject.keywords | intrusion detection | |
| dc.subject.keywords | network security | |
