DocumentCode :
3101870
Title :
Real-time anomaly detection using soft-computing techniques
Author :
Copeland, John A. ; Garcia, Raymond C.
Author_Institution :
Commun. Syst. Center, Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2001
fDate :
2001
Firstpage :
105
Lastpage :
108
Abstract :
This body of work sheds light on the current problems related to network security and applies intelligent tools to some of these problems. While the use of intelligent tools in network security is new, the few papers that have been published offer promising results. Of particular interest is the application of neural networks, fuzzy logic, and evolutionary computation to network security problems (sniffing, IP spoofing, denial of service attacks, etc.). The work here develops a technique that utilizes soft computing tools in order to detect anomalous network behavior, detect and have real-time applicability
Keywords :
Internet; evolutionary computation; fuzzy logic; learning (artificial intelligence); neural nets; real-time systems; security of data; telecommunication security; transport protocols; vector quantisation; IP spoofing; Internet; LVQ neural network; TCP sequence number prediction; anomalous network behavior detection; denial of service attacks; evolutionary computation; fuzzy logic; intelligent tools; learning vector quantizer; network security; real-time anomaly detection; sniffing; soft-computing techniques; Communication system security; Computer crime; Evolutionary computation; Fuzzy logic; Information security; Intelligent networks; Intrusion detection; Monitoring; Neural networks; TCPIP;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SoutheastCon 2001. Proceedings. IEEE
Conference_Location :
Clemson, SC
Print_ISBN :
0-7803-6748-0
Type :
conf
DOI :
10.1109/SECON.2001.923097
Filename :
923097
Link To Document :
بازگشت