Due　to　the　excellent　performance　of　the　HMM(Hidden　Markov　Model)　in　pattern　recognition,　it　has　been　widely　used　in　voice　recognition,　text　recognition.　In　recent　years,　the　HMM　has　also　been　applied　to　the　intrusion　detection.　The　intrusion　detection　method　based　on　the　HMM　is　more　efficient　than　other　methods.　The　HMM　based　intrusion　detection　method　is　composed　by　two　processes:　one　is　the　HMM　process;　the　other　is　the　hard　decision　process,　which　is　based　on　the　profile　database.　Because　of　the　dynamical　behavior　of　system　calls,　the　hard　decision　process　based　on　the　profile　database　cannot　be　efficient　to　detect　novel　intrusions.　On　the　other　hand,　the　profile　database　will　consume　many　computer　resources.　For　these　reasons,　the　combined　detection　method　was　provided　in　this　paper.　The　neural　network　is　a　kind　of　artificial　intelligence　tools　and　is　combined　with　the　HMM　to　make　soft　decision.　In　the　implementation,　radial　basis　function　model　is　used,　because　of　its　simplicity　and　its　flexibility　to　adapt　pattern　changes.　With　the　soft　decision　based　on　the　neural　network,　the　robustness　and　accurate　rate　of　detection　model　network,　the　robustness　and　accurate　rate　of　detection　model　are　greatly　improved.　The　efficiency　of　this　method　has　been　evaluated　by　the　data　set　originated　from　Hunan　Technology　University.　©　Springer-Verlag　Berlin　Heidelberg　2005.