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by\u0026nbsp; day.\u0026nbsp; Intrusion\u0026nbsp; detection\u003cbr\u003e\ntechnology\u0026nbsp; is\u0026nbsp; an\u0026nbsp; effective\u0026nbsp; approach\u0026nbsp; to\u0026nbsp; dealing\u0026nbsp; with\u0026nbsp; the\u003cbr\u003e\nproblems\u0026nbsp; of\u0026nbsp; network\u0026nbsp; security.\u0026nbsp; In\u0026nbsp; this\u0026nbsp; paper,\u0026nbsp; performance\u003cbr\u003e\nanalysis\u0026nbsp; of\u0026nbsp; the\u0026nbsp; proposed\u0026nbsp; anomaly\u0026nbsp; based\u0026nbsp; intrusion\u0026nbsp; detection\u003cbr\u003e\nsystem\u0026nbsp; using\u0026nbsp; modified\u0026nbsp; FP-Growth\u0026nbsp; algorithm\u0026nbsp; is\u0026nbsp; carried\u0026nbsp; out\u003cbr\u003e\nbased\u0026nbsp; on\u0026nbsp; several\u0026nbsp; parameters.\u0026nbsp; The\u0026nbsp; proposed\u0026nbsp; intrusion\u003cbr\u003e\ndetection\u0026nbsp; system\u0026nbsp; is\u0026nbsp; composed\u0026nbsp; of\u0026nbsp; three\u0026nbsp; main\u0026nbsp; parts:\u003cbr\u003e\npreprocessing,\u0026nbsp; normal-attack\u0026nbsp; detection\u0026nbsp; and\u0026nbsp; attack\u003cbr\u003e\nclassification.\u0026nbsp; KDD\u0026nbsp; Cup\u0026rsquo;99\u0026nbsp; datasets\u0026nbsp; are\u0026nbsp; used\u0026nbsp; as\u0026nbsp; reference\u003cbr\u003e\ninput\u0026nbsp; dataset\u0026nbsp; to\u0026nbsp; experimentally\u0026nbsp; analyze\u0026nbsp; the\u0026nbsp; performance\u0026nbsp; of\u003cbr\u003e\nthe\u0026nbsp; system.\u0026nbsp; Experimental\u0026nbsp; results\u0026nbsp; show\u0026nbsp; that\u0026nbsp; the\u0026nbsp; proposed\u003cbr\u003e\nintrusion\u0026nbsp; detection\u0026nbsp; system\u0026nbsp; offers\u0026nbsp; preferable\u0026nbsp; accuracy\u0026nbsp; and\u003cbr\u003e\nfalse alarm rate while reducing the execution time.\u003c/p\u003e"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "FP-Growth algorithm"}, {"interim": "intrusion detection"}, {"interim": "data mining"}, {"interim": "anomaly"}, {"interim": "KDD-99 dataset"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-04"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm.pdf", "filesize": [{"value": "514 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 514000.0, "url": {"url": "https://meral.edu.mm/record/3114/files/Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm.pdf"}, "version_id": "618525b0-0b1f-477f-b032-5e115f0432ad"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICSE", "subitem_c_date": "12-13 December, 2015", "subitem_conference_title": "International Conference on Science and Engineering", "subitem_part": "", "subitem_place": "Myanmar", "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": "Khin Moh Moh Aung"}, {"subitem_authors_fullname": "Nyein Aye Maung Maung"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015-12-12"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.5281/zenodo.3268391"}, "item_title": "Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm", "item_type_id": "21", "owner": "1", "path": ["1596119372420"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003114", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-04"}, "publish_date": "2019-07-04", "publish_status": "0", "recid": "3114", "relation": {}, "relation_version_is_last": true, "title": ["Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm"], "weko_shared_id": -1}
Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm
http://hdl.handle.net/20.500.12678/0000003114
http://hdl.handle.net/20.500.12678/0000003114683e013e-4d68-452a-aa3f-e599fc5073cd
d0ad1fa5-aaed-4474-b23b-9c3d061d5efd
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Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm.pdf (514 Kb)
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm | |||||
Language | en | |||||
Publication date | 2015-12-12 | |||||
Authors | ||||||
Khin Moh Moh Aung | ||||||
Nyein Aye Maung Maung | ||||||
Description | ||||||
<p>Nowadays, research area on network security is<br> facing new challenges with the rapid increment of internet<br> usage and possible attacks day by day. Intrusion detection<br> technology is an effective approach to dealing with the<br> problems of network security. In this paper, performance<br> analysis of the proposed anomaly based intrusion detection<br> system using modified FP-Growth algorithm is carried out<br> based on several parameters. The proposed intrusion<br> detection system is composed of three main parts:<br> preprocessing, normal-attack detection and attack<br> classification. KDD Cup’99 datasets are used as reference<br> input dataset to experimentally analyze the performance of<br> the system. Experimental results show that the proposed<br> intrusion detection system offers preferable accuracy and<br> false alarm rate while reducing the execution time.</p> |
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Keywords | ||||||
FP-Growth algorithm, intrusion detection, data mining, anomaly, KDD-99 dataset | ||||||
Identifier | 10.5281/zenodo.3268391 | |||||
Journal articles | ||||||
Conference papers | ||||||
ICSE | ||||||
12-13 December, 2015 | ||||||
International Conference on Science and Engineering | ||||||
Myanmar | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |