This correspondence presents an extended pipelined second-order Volterra (EPSOV) filter for active control of nonlinear noise processes. The corresponding nonlinear filtered-x algorithms using the filter bank implementation are also suggested. Compared to the standard SOV filter using the filtered-x least mean square (SOVFXLMS), those modules of the EPSOV filter can be performed simultaneously in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Results obtained from computer simulations for nonlinear noise processes demonstrate that the proposed method outperforms the SOV.

A new light-weight approach, based on the cross-correlation of system call sequences, is presented to identify normal or intrusive program behavior. The program behavior is represented by the cross-correlation value which can be used to indicate the similarity between two sequences. If two sequences are same, the cross-correlation between them will achieve the maximum value. This method of characterizing program behavior by using cross-correlation offers significant computational advantages over HMM (hidden Markov model) or NN (neural network) methods due to the absence of unnecessary training process. Our experiments using UNM (University of New Mexico) audit data show that the cross-correlation based method can effectively detect intrusive attacks and achieve a low false positive rate.