Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "7779be93-6da4-4e7b-b59c-2771b8c248e9"}, "_deposit": {"id": "4468", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4468"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4468", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Due to continuous growth of the Internettechnology, it needs to establish securitymechanism. However, many current intrusiondetection systems (IDSs) are rule-based systems,which have limitations to detect novel intrusions.Moreover, encoding rules is time-consuming andhighly depends on the knowledge of knownintrusions. Therefore, we propose new systematicframework that apply a data mining algorithmcalled random forests (RF) and Support VectorMachine (SVM). This system uses RandomForests (RF) for feature selection and parameteroptimization and Support Vector Machine (SVM)for intrusion detection. RF provides the variableimportance by numeric values so that theirrelevant features can be eliminated. SupportVector Machines (SVM) as a classical patternrecognition tool have been widely used forintrusion detection. First, RF is utilized topreprocess the data and select the mostimportant features to eliminate the insignificantfeatures and optimize parameters. Second, SVMmodel is used to learn and detect intrusion usingselected important features."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Intrusion Detection Systems"}, {"interim": "Random Forests"}, {"interim": "Support Vector Machine"}, {"interim": "Feature Selection"}, {"interim": "Parameter Optimization"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-11-13"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "10040.pdf", "filesize": [{"value": "421 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 421000.0, "url": {"url": "https://meral.edu.mm/record/4468/files/10040.pdf"}, "version_id": "47d93837-dc3a-4633-b96a-5490e38da59f"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Tenth International Conference On Computer Applications (ICCA 2012)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Oo, May Mar"}, {"subitem_authors_fullname": "Yi, Aye Mon"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2012-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/2398"}, "item_title": "A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004468", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-11-13"}, "publish_date": "2019-11-13", "publish_status": "0", "recid": "4468", "relation": {}, "relation_version_is_last": true, "title": ["A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine

http://hdl.handle.net/20.500.12678/0000004468
http://hdl.handle.net/20.500.12678/0000004468
fa1f80b4-e949-4f67-ba96-5dc1f3b455fc
7779be93-6da4-4e7b-b59c-2771b8c248e9
None
Preview
Name / File License Actions
10040.pdf 10040.pdf (421 Kb)
Publication type
Article
Upload type
Publication
Title
Title A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine
Language en_US
Publication date 2012-02-28
Authors
Oo, May Mar
Yi, Aye Mon
Description
Due to continuous growth of the Internettechnology, it needs to establish securitymechanism. However, many current intrusiondetection systems (IDSs) are rule-based systems,which have limitations to detect novel intrusions.Moreover, encoding rules is time-consuming andhighly depends on the knowledge of knownintrusions. Therefore, we propose new systematicframework that apply a data mining algorithmcalled random forests (RF) and Support VectorMachine (SVM). This system uses RandomForests (RF) for feature selection and parameteroptimization and Support Vector Machine (SVM)for intrusion detection. RF provides the variableimportance by numeric values so that theirrelevant features can be eliminated. SupportVector Machines (SVM) as a classical patternrecognition tool have been widely used forintrusion detection. First, RF is utilized topreprocess the data and select the mostimportant features to eliminate the insignificantfeatures and optimize parameters. Second, SVMmodel is used to learn and detect intrusion usingselected important features.
Keywords
Intrusion Detection Systems, Random Forests, Support Vector Machine, Feature Selection, Parameter Optimization
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/2398
Journal articles
Tenth International Conference On Computer Applications (ICCA 2012)
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 14:52:39.452414
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL