Host Based Intrusion Detection Using Support Vector Machines (Record no. 55963)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02230nam a22001337a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Memon, Saifullah |
| -- | 12MSIT20 |
| -- | Supervisor - Syed Raheel Hassan |
| 245 ## - TITLE STATEMENT | |
| Title | Host Based Intrusion Detection Using Support Vector Machines |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Nawabshah: |
| Name of publisher | QUEST, |
| Year of publication | 2015. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 68p, : |
| 500 ## - GENERAL NOTE | |
| General note | ABSTRACT<br/>Information safety, security and stability of the systems have been a serious is from and individual to an enterprise and are also remained a serious concern<br/>globally. With the rapid growth of Internet the complexity, availabili y, and accessibility have helped to raise the safekeeping risk of information systems enormously. Accessing any network there always has been a threat of attac that<br/>compromise confidentiality, proper working of the system and resources. o<br/> safeguard against all possible intrusions there has been number of different ways like firewall, antivirus and Intrusion Detection Systems (IDS). To make the information<br/>system safe and secure, the intrusion detection acts as critical component. KDD intrusion detection dataset offers labeled data for the scientists and researchers, choosing most important features or patterns from input dataset makes problem simpler, faster and acquires much more accuracy towards threat detection. In our work we demonstrate the problem of recognizing most important input patterns to design a more efficient Intrusion Detection System (IDS). Consequently, removal of irrelevant or unimportant inputs makes the problem of detecting a threat simpler, faster, and accurate. It has been an important issue in the domain of intrusion detection that features selection and ranking must be made accordingl y; because, it is the only way left to detect intrusion accurately and efficiently. We implement the<br/>procedure to remove one feature at a time to run experiments on upport Vector Machine (SVM) to grade the significance of the features for the KDD collected intrusion dataset. It is revealed that SVM based IDSs utilizin g a lesser number of features could give improved and efficient performance ce.<br/><br/>xiii<br/> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Department Of Information Technology |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://tinyurl.com/4ud5m7nm |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Thesis and Dissertation |
| Withdrawn status | Lost status | Home library | Current library | Date acquired | Accession Number | Koha item type |
|---|---|---|---|---|---|---|
| Research Section | Research Section | 23/11/2016 | MP/07-68 | Thesis and Dissertation | ||
| Research Section | Research Section | 25/09/2018 | MP/25-264 | Thesis and Dissertation |