Paper Info
Title
Using genetic algorithm for network status learning and worm virus detection scheme
Abstract
This paper tries to propose the worm virus detection system that focuses on many connection attempts, more frequently occurring in the process of scanning than their common transmission processes. And this paper tries to determine the critical value of connection attempt by using the ordinary time network traffic learning technique which applies the genetic algorithm in order to ensure accurate detection of virus, depending on the status of network. This system can reduce the damage from worm virus more quickly than the pattern-founded worm virus detection system because it applies the common characteristics of worm viruses to detect them, and the criteria for judgment can be altered in its application though the network may change.
Year
DOI
Venue
2006
10.1007/11875581_54
IDEAL
Keywords
Field
DocType
genetic algorithm,common characteristic,critical value,ordinary time network traffic,network status,connection attempt,worm virus detection scheme,worm virus detection system,common transmission process,accurate detection,pattern-founded worm virus detection,worm virus
Information system,Virus,Computer science,Artificial intelligence,Intrusion detection system,Machine learning,Genetic algorithm
Conference
Volume
ISSN
ISBN
4224
0302-9743
3-540-45485-3
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Donghyun Lim121.09
Jinwook Chung23714.89
Seongjin Ahn34420.98