Presentation 2004/11/27
A Machine Learning Approach to Anti-virus System(Artificial Intelligence I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
HOANG KIEMT, THUY NGUYEN THANH, TRUONG MINH NHAT QUANG,
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Abstract(in English) As the development of Internet nowadays, computer viruses have been a hot story. They more and more seriously infect, destroy and steal data from many computer systems in the world. Therefore, it is necessary to improve the identifying method of an anti-virus system. In this paper, we would like to introduce ah intelligent anti-virus system by using Machine Learning Rule-based Approach. The basic tasks of this project consist of modeling knowledge base, forming rule sets to recognize known viruses, diagnosing and discovering interesting rules from virus database using Data Mining algorithms, finding hidden attributes/behaviors and predicting unknown viruses with Genetic Programming tools.
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Keyword(in English) Machine Learning / Genetic Programming / Data Mining / Computer Virus / Anti-virus
Paper # AI2004-29
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Committee AI
Conference Date 2004/11/27(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Machine Learning Approach to Anti-virus System(Artificial Intelligence I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
Sub Title (in English)
Keyword(1) Machine Learning
Keyword(2) Genetic Programming
Keyword(3) Data Mining
Keyword(4) Computer Virus
Keyword(5) Anti-virus
1st Author's Name HOANG KIEMT
1st Author's Affiliation University of Natural Sciences Ho Chi Minh City()
2nd Author's Name THUY NGUYEN THANH
2nd Author's Affiliation Hanoi University of Technology
3rd Author's Name TRUONG MINH NHAT QUANG
3rd Author's Affiliation Cantho Inservice University
Date 2004/11/27
Paper # AI2004-29
Volume (vol) vol.104
Number (no) 485
Page pp.pp.-
#Pages 5
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