The Research Of Network Intrusion Detection Method Based On Data Mining

The　paper　applies　data　mining　to　intrusion　detection,and　analyzes　respectively　the　application　of　clustering　analysis　and　association　rules　analysis　in　intrusion　detection.We　improve　the　K-means　clustering　arithmetic　and　prove　that　the　improved　clustering　arithmetic　can　advance　the　detection　rate　through　the　detection　of　the　KDD　cup　99　data.The　paper　also　presents　the　detection　method　of　combining　clustering　analysis　with　association　rules　and　the　model　of　intrusion　detection　based　on　this　method.First　mine　the　relation　of　symbol　attribute　from　the　known　training　data　through　Apriori　association　rules　arithmetic,and　establish　normal　model　and　abnormal　model　respectively,then　patition　the　clustering　result　again　according　to　the　model,so　that　it　can　reach　the　better　result.Finally　the　detection　of　　KDD　cup　99　data　indicate　that　this　detection　model　can　detect　the　DoS　attack　and　the　Probing　attack　in　a　high　detection　rate　and　a　low　wrong　detection　rate,so　it　can　resolve　the　contradiction　of　detection　rate　and　wrong　detection　rate　effectively.