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
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;
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
DOI :
10.1109/CEC.2007.4424541