Network intrusion detection in high dimensional space

We propose and evaluate an approach for network intrusion detection in high dimensional space. This approach is based on an approximate solution to the nearest neighbor problem. Our evaluation is based on the KDD'99 data set, a Yahoo Web spam data set and another set used in the NIPS'03 feature selection challenge. The approximate approach shows that good performance in terms of detection rate and false positives rate can be achieved.