In　industrial　control　systems　(ICSs),　intrusion　detection　is　a　vital　task.　Conventional　intrusion　detection　systems　(IDSs)　rely　on　manually　designed　rules.　These　rules　heavily　depend　on　professional　experience,　thereby　making　it　challenging　to　represent　the　increasingly　complicated　industrial　control　logic.　Although　deep　learning-based　approaches　provide　better　accuracy　than　other　methods,　they　can　only　provide　alerts.　However,　they　cannot　provide　administrators　with　detailed　information.　In　this　study,　we　propose　the　logic　understanding　IDS　(LU-IDS),　which　is　a　rule-based　IDS　with　in-depth　understandings　of　industrial　control　logic.　Our　proposed　LU-IDS　uses　a　specially　designed　deep　learning-based　model　to　capture　features　automatically　and　carry　out　attack　classification.　More　importantly,　it　analyzes　the　knowledge　learned　from　the　classification　of　attacks　to　understand　the　abnormal　industrial　control　logic　and　generate　rules.　The　experimental　results　indicate　that　our　proposed　LU-IDS　demonstrates　excellent　performance　on　intrusion　detection.　The　rules　generated　by　our　proposed　LU-IDS　can　be　used　to　successfully　detect　all　types　of　attacks　on　two　public　datasets.　Author