The　integration　of　industrialization　and　informatization　has　exposed　industrial　control　systems　(ICSs)　to　increasingly　serious　security　challenges.　Currently,　the　mainstream　method　to　protect　the　security　of　ICSs　is　intrusion　detection　system　(IDS)　based　on　deep-learning.　However,　these　methods　depend　on　a　massive　amount　of　high-quality　data.　Owing　to　the　characteristics　and　protocol　limitations,　ICSs　data　usually　experience　low-quality　and　data　imbalance　problems,　which　significantly　affects　the　accuracy　of　IDS.　In　this　study,　an　IDS　for　ICS　that　combines　data　expansion　algorithm　and　CNN　was　proposed.　A　novel　normalized　neighborhood　weighted　convex　combined　random　sample　(NNW-CCRS)　oversampling　algorithm　was　designed,　which　automatically　attenuates　the　effects　of　noise　and　expanding　imbalanced　data　to　produce　balanced　ICS　datasets.　By　reducing　the　impact　of　imbalanced　ICS　data　on　IDSs,　our　system　effectively　protects　the　security　of　ICS.　Secure　Water　Treatment　dataset　(SWaT)　was　used　for　experimental　validation.　The　experimental　results　confirmed　that　the　accuracy　of　the　proposed　system　improved　by　approximately　20%,　compared　to　the　ICS　without　data　expansion.