DocumentCode
2689165
Title
A genetic algorithm for solving the binning problem in networked applications detection
Author
Shevertalov, Maxim ; Stehle, Edward ; Mancoridis, Spiros
Author_Institution
Drexel Univ., Philadelphia
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
713
Lastpage
720
Abstract
Network administrators need a tool that detects the kind of applications running on their networks, in order to allocate resources and enforce security policies. Previous work shows that applications can be detected by analyzing packet size distributions. Detection by packet size distribution is more efficient and accurate if the distribution is binned. An unbinned packet size distribution considers the occurrences of each packet size individually. In contrast, a binned packet size distribution considers the occurrences of packets within packet size ranges. This paper reviews some of the common methods for binning distributions and presents an improved approach to binning using a genetic algorithms to assist the detection of network applications.
Keywords
genetic algorithms; resource allocation; security of data; binned packet size distribution; binning problem; genetic algorithm; network administrators; networked applications detection; resource allocation; security policies; Application software; Classification algorithms; Computer science; Educational institutions; Frequency; Genetic algorithms; Genetic engineering; Histograms; Resource management; Telecommunication traffic;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location
Singapore
Print_ISBN
978-1-4244-1339-3
Electronic_ISBN
978-1-4244-1340-9
Type
conf
DOI
10.1109/CEC.2007.4424541
Filename
4424541
Link To Document