With　the　ever　growing　demand　of　location-independent　access　to　Autonomous　Decentralized　Systems　(ADS),　anomaly　detection　scheme　for　industrial　Ethernet,　which　highly　is　satisfied　with　demanding　real-time　and　reliable　industrial　applications,　becomes　one　of　the　most　pressing　subjects　in　ADS.　In　this　paper,　we　present　an　innovative　approach　to　build　a　traffic　model　based　on　structural　time　series　model　for　a　chemical　industry　system.　A　basic　structural　model　that　decomposes　time　series　into　four　items　is　established　by　the　stationary　analysis　of　industrial　traffic.　Parameters　in　the　model　are　identified　by　the　state　space　model,　which　is　conducted　from　the　training　sequence　using　standard　Kalman　filter　recursions　and　the　EM　algorithm.　Furthermore,　the　performance　of　state　space　model　is　evaluated　by　the　experimental　results　that　confirm　significant　improvement　in　detection　accuracy　and　the　validity　of　abnormal　data　localization.　(C)　2016　Elsevier　B.V.　All　rights　reserved.