Anomaly clustering based on correspondence analysis
This thesis is submitted in partial fulfilment of the requirements for the degree of Masters of Science in Electrical and Electronic Engineering, 2016.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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BRAC Univeristy
2018
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/9276 |
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10361-92762019-09-30T03:15:35Z Anomaly clustering based on correspondence analysis Islam, Humayra Ahmed, Dr. Tarem Department of Electrical and Electronic Engineering, BRAC University Anomaly Cluster QR code Clustering algorithm This thesis is submitted in partial fulfilment of the requirements for the degree of Masters of Science in Electrical and Electronic Engineering, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (page 70-72). The huge amount of traffic in backbone IP networks produces various kinds of anomalies in data packets. Distinct classifiers have been developed to deal with this anomalous data. These classifiers typically have predefined number of classes and use supervised learning methods. Some classifiers apply windowing method to make the huge data scalable into small groups. In this work, a new method for the classification of anomalous data have been applied with unsupervised learning using Correspondence Analysis (CA). Correspondence Analysis does not need a predefined number of clusters to begin with, and can handle comparatively large amounts of data. Results have been compared with other clustering techniques, which are applied on real data from the US Abilene backbone network. The results indicate that the proposed method is promising in classifying anomalies on the basis of frequencies of anomalous facade. Humayra Islam M. Electrical and Electronic Engineering 2018-01-29T05:02:27Z 2018-01-29T05:02:27Z 2016 2016-06 Thesis ID 11261008 http://hdl.handle.net/10361/9276 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 72 pages application/pdf BRAC Univeristy |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Anomaly Cluster QR code Clustering algorithm |
| spellingShingle |
Anomaly Cluster QR code Clustering algorithm Islam, Humayra Anomaly clustering based on correspondence analysis |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Masters of Science in Electrical and Electronic Engineering, 2016. |
| author2 |
Ahmed, Dr. Tarem |
| author_facet |
Ahmed, Dr. Tarem Islam, Humayra |
| format |
Thesis |
| author |
Islam, Humayra |
| author_sort |
Islam, Humayra |
| title |
Anomaly clustering based on correspondence analysis |
| title_short |
Anomaly clustering based on correspondence analysis |
| title_full |
Anomaly clustering based on correspondence analysis |
| title_fullStr |
Anomaly clustering based on correspondence analysis |
| title_full_unstemmed |
Anomaly clustering based on correspondence analysis |
| title_sort |
anomaly clustering based on correspondence analysis |
| publisher |
BRAC Univeristy |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9276 |
| work_keys_str_mv |
AT islamhumayra anomalyclusteringbasedoncorrespondenceanalysis |
| _version_ |
1814309030595657728 |