a method of extracting malicious expressions in bulletin board systems by using context analysis
DESCRIPTION
A method of extracting malicious expressions in bulletin board systems by using context analysis. Presenter: Jun-Yi Wu Authors: Hiroshi Hanafusa , Kazuhiro Morita, Masao Fuketa , Jun- ichi Aoe. 國立雲林科技大學 National Yunlin University of Science and Technology. 2011 IPM. Outline. Motivation - PowerPoint PPT PresentationTRANSCRIPT
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
A method of extracting malicious expressions in bulletin board systems by using context analysis
Presenter: Jun-Yi Wu Authors: Hiroshi Hanafusa, Kazuhiro Morita, Masao Fuketa, Jun-ichi Aoe
2011 IPM
國立雲林科技大學National Yunlin University of Science and Technology
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
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I. M.Motivation
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The extracting scheme of the traditional method depends on words or a sequence of words without considering contexts of articles.
To takes a lot of human efforts to alert malicious articles.
Malicious expression text Non-malicious expression text
Intelligent Database Systems Lab
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I. M.Objective
To presents a new context filtering algorithm to reduce the effort of human and to improve the rate of false positive without degrading the rate of false negative.
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Intelligent Database Systems Lab
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I. M.Methodology
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The presented system Rule-based extracting knowledge Multi-attribute matching
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I. M.Methodology
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The presented system
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The presented system The outline of context analysis
Malicious expression text Non-malicious expression text
Intelligent Database Systems Lab
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The presented system Inadequate and crime expressions
Inadequate Crime
Abuse、 Discrimination、 Dating Service Website、 Obscenity
Murder&Violence、 Explosion&Arson、 Crime Material、 Drug
Intelligent Database Systems Lab
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I. M.Methodology
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Rule-based extracting knowledge Definition of multi-attribute rules
For example : “He kills someone”
STR: string, or, word spelling.CAT: category by general concepts, or a part of speeches.SEM: sematic information
Intelligent Database Systems Lab
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I. M.Methodology
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Multi-attribute matching Construction of machines(MAPM) Goto and out function
Intelligent Database Systems Lab
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Multi-attribute matching Procedure
For example : “I get a strong sward”
Intelligent Database Systems Lab
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I. M.Experiments
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I. M.Experiments
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I. M.Experiments
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I. M.Experiments
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Time evaluation and error analysis
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I. M.Conclusion
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The presented method MULTI is a very useful approach for filtering services for inadequate expressions.
It is difficult task to register new words and expressions into dictionaries together with their categories and semantics.
The rules bases of the presented method MULTI is building for frequent expressions step by step, but there are difficult problems as shown in the following examples:
‘‘RQJmcf2O” kill ‘‘Aaaaqqqbbb”
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
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Advantage Many examples
Drawback Some mistakes
Application Information retrieval Context analysis