'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Several of today’s computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication net- works 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
