Intrusion　detection　is　essential　for　ensuring　the　security　of　industrial　control　systems.　However,　conventional　intrusion　detection　approaches　are　unable　to　cope　with　the　complexity　and　ever-changing　nature　of　industrial　intrusion　attacks.　In　this　study,　we　propose　an　industrial　control　intrusion　detection　approach　based　on　a　combined　deep　learning　model　for　communication　processes　that　use　the　Modbus　protocol.　Initially,　the　network　packets　are　classified　as　carrying　information　and　noncarrying　information　based　on　key　fields　according　to　the　communication　protocol　used.　Next,　a　template　comparison　approach　is　employed　to　detect　the　network　packets　that　do　not　carry　any　information.　Furthermore,　an　approach　based　on　a　GoogLeNet-long　short-term　memory　model　is　used　to　detect　the　network　packets　that　do　carry　information.　This　approach　involves　network　packet　sequence　construction,　feature　extraction,　and　time-series　level　detection.　Subsequently,　the　detected　intrusions　are　classified　into　multiple　categories　through　a　Softmax　classifier.　A　gas　pipeline　dataset　of　the　Modbus　protocol　is　used　to　evaluate　the　proposed　approach　and　compare　it　with　existing　strategies.　The　accuracy,　false-positive　rate,　and　miss　rate　are　97.56%,　2.42%,　and　2.51%,　respectively,　thus　confirming　that　the　proposed　approach　is　suitable　for　intrusion　detection　in　industrial　control　systems.