Density estimation can be used to make sense of data collected by large scale systems. An estimate of the underlying probability density function can be used to characterize normal network operating conditions. In this paper, we present a recursive method for constructing and updating an estimate of the non-stationary high dimensional probability density function using parallel programming. Once we have characterized standard operating conditions we perform real time checks for changes. We demonstrate the effectiveness of the approach via the use of simulated data as well as data from Internet header packets.
['Kyle Caudle', 'Christer Karlsson', 'Larry D. Pyeatt']
Using Density Estimation to Detect Computer Intrusions