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    •   BracU IR
    • School of Engineering (SoE)
    • Department of Electrical and Electronic Engineering (EEE)
    • Thesis & Report, MSc (Electrical and Electronic Engineering)
    • View Item
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    Anomaly clustering based on correspondence analysis

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    11261008_EEE.pdf (2.113Mb)
    Date
    2016-06
    Publisher
    BRAC Univeristy
    Author
    Islam, Humayra
    Metadata
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    URI
    http://hdl.handle.net/10361/9276
    Abstract
    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.
    Keywords
    Anomaly; Cluster; QR code; Clustering algorithm
     
    Description
    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).
    Department
    Department of Electrical and Electronic Engineering, BRAC University
    Collections
    • Thesis & Report, MSc (Electrical and Electronic Engineering)

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