TY - GEN
T1 - A biologically inspired denial of service detector using the random neural network
AU - Loukas, Georgios
AU - Öke, Gülay
PY - 2007
Y1 - 2007
N2 - Several of todays' computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication networks have started applying biologically-inspired methods to achieve some of their self-* properties. We build upon such methods to solve the recent problem of detection of Denial of Service networking attacks, by proposing a combination of Bayesian decision making and the Random Neural Networks (RNN) which are inspired by the random spiking behaviour of the biological neurons. Our approach is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using different architectures of the RNN. The experiments are conducted using the CPN networking protocol which is also based on the RNN.
AB - Several of todays' computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication networks have started applying biologically-inspired methods to achieve some of their self-* properties. We build upon such methods to solve the recent problem of detection of Denial of Service networking attacks, by proposing a combination of Bayesian decision making and the Random Neural Networks (RNN) which are inspired by the random spiking behaviour of the biological neurons. Our approach is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using different architectures of the RNN. The experiments are conducted using the CPN networking protocol which is also based on the RNN.
UR - http://www.scopus.com/inward/record.url?scp=50249130274&partnerID=8YFLogxK
U2 - 10.1109/MOBHOC.2007.4428683
DO - 10.1109/MOBHOC.2007.4428683
M3 - Conference contribution
AN - SCOPUS:50249130274
SN - 1424414555
SN - 9781424414550
T3 - 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS
BT - 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS
T2 - 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS
Y2 - 8 October 2007 through 11 October 2007
ER -