Identifying image spam based on header and file properties using C4.5 decision trees and support vector machine learning

Proceedings of the 2007 IEEE Workshop on Information Assurance, IAW

Volumn , Issue , 2007, Pages 255-261

  Krasser, Sven ^a   Tang, Yuchun ^a   Gould, Jeremy ^a   Alperovitch, Dmitri ^a   Judge, Paul ^a  

^a Secure Computing Corporation   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DECISION MAKING; DECISION THEORY; DECISION TREES; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE RETRIEVAL; INTERNET; LEARNING SYSTEMS; SPAMMING; SUPPORT VECTOR MACHINES; VECTORS;

BANDWIDTH REQUIREMENTS; CLASSIFICATION PERFORMANCE; COMPLEX IMAGES; COMPUTATIONALLY INEXPENSIVE; DECODED IMAGES; E MAIL COMMUNICATIONS; FEATURE EXTRACTION AND CLASSIFICATION; IMAGE FILES; INFORMATION ASSURANCE (IA); INFORMATION GAIN (IG); MACHINE-LEARNING; MORPHOLOGICAL FEATURES; SUPPORT VECTOR MACHINE (SVM) CLASSIFIERS;

FEATURE EXTRACTION;

EID: 48749127882     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IAW.2007.381941     Document Type: Conference Paper

References (6)

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4. R. J. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
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6. A. P. Bradley, "The use of the area under the roc curve in the evaluation of machine learning algorithms.," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997.