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E-Mail Worm Detection Using Data Mining
Mohammad M. Masud
, Latifur Khan
, Bhavani Thuraisingham
Research output
:
Contribution to journal
›
Article
›
peer-review
4
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Citations (Scopus)
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Keyphrases
Worm
100%
Worm Detection
100%
Dimensionality Reduction
66%
Phase Selection
66%
Data Mining Techniques
33%
Support Vector Machine
33%
Principal Coordinate Analysis (PCoA)
33%
Published Results
33%
Redundancy
33%
Classification Methods
33%
Learning Classifiers
33%
Classification Model
33%
Novel Combinations
33%
Selection Algorithm
33%
Decision Tree
33%
Support Vector Machine Classification
33%
Presence-absence
33%
Feature Selection
33%
Noise Reduction
33%
Number of Words
33%
Number of Features
33%
Feature Selection Methods
33%
Efficient Classification
33%
Body-subject
33%
Feature Dimension
33%
Greedy Selection
33%
Tree Selection
33%
Attachment Style
33%
Reduced Data
33%
Nave Bayes
33%
Computer Science
Data Mining
100%
Support Vector Machine
100%
Feature Selection
100%
Selection Phase
100%
Data Mining Technique
50%
Classification Technique
50%
Classification Models
50%
Algorithm Selection
50%
Component Analysis
50%
Principal Components
50%
Decision Trees
50%
Attachment Type
50%
Feature Dimension
50%
Nave Bayes
50%