Machine　learning　techniques　are　widely　used　for　network　intrusion　detection　(NID).　However,　it　has　to　face　the　unbalance　of　training　samples　between　classes　as　it　is　hard　to　collect　samples　of　some　intrusion　classes.　This　would　produce　false　positives　for　these　intrusion　classes.　Meanwhile,　since　there　are　various　types　of　intrusions,　classification　boundaries　between　different　classes　are　seriously　nonlinear.　Due　to　the　huge　amount　of　training　data,　computational　efficiency　is　also　required.　This　paper　therefore　proposes　an　efficient　cascaded　classifier　for　NID.　This　classifier　consists　of　a　collection　of　binary　base　classifiers　which　are　serially　connected.　Each　base　classifier　corresponds　to　a　type　of　intrusion.　The　order　of　these　base　classifiers　is　automatically　determined　based　on　the　number　of　false　positives　to　cope　with　the　unbalance　of　training　samples.　Extreme　learning　machine　algorithm,　which　has　low　computational　cost,　is　used　to　train　these　base　classifiers　to　delineate　the　nonlinear　boundaries　between　classes.　This　proposed　NID　method　is　evaluated　on　the　KDD99　data　set.　Experimental　results　have　shown　that　this　proposed　method　outperforms　other　state-of-the-art　methods　including　decision　tree,　back-propagation　neural　network　and　support　vector　machines.