Name
Abstract | ||
|---|---|---|
Border Gateway Protocol (BGP), which is the defacto standard inter-domain routing protocol in the Internet today, has severe problems, such as worm viruses, denial of service (DoS) attacks, etc. To ensure the stability and security of the inter-domain routing system in the autonomy system, it is critical to accurately and quickly detect abnormal BGP events. In this paper, a novel feature selection algorithm based on the asymmetric entropy named FSAMI is proposed to evaluate the characteristics of describing the BGP abnormal events, which is independent on the machine learning methods. Meanwhile the under-sampling, neural network (NN) and feature selection are introduced to predict BGP abnormal activities to treat the imbalance problem. Numerical experimental results on RIPE archive data set show that the FSAMI method improves the g_means values of abnormal events detection and helps to improve the prediction ability. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03348-3_56 | ADMA |
Keywords | Field | DocType |
inter-domain routing system,feature selection,bgp abnormal event,autonomy system,fsami method,bgp abnormal events,abnormal events detection,bgp abnormal activity,novel feature selection algorithm,defacto standard inter-domain,abnormal bgp event,asymmetric feature selection,machine learning,neural network,mutual information,dos attack,bgp,neural networks,border gateway protocol,denial of service | Network mapping,De facto standard,Data mining,Feature selection,Denial-of-service attack,Computer science,Computer network,Border Gateway Protocol,Artificial neural network,Routing protocol,The Internet | Conference |
Volume | ISSN | Citations |
5678 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |