With　the　rapid　development　of　Industry　4.0,　the　importance　of　cyber　security　for　industrial　control　systems　has　become　increasingly　prominent.　The　complexity　and　diversity　of　industrial　control　systems　result　in　data　with　high　dimensionality　and　strong　correlation,　posing　significant　challenges　in　obtaining　labeled　data.　However,　current　intrusion　detection　methods　often　demand　large　amounts　of　labeled　data　for　effective　training.　To　address　this　limitation,　this　paper　proposes　a　semi-supervised　anomaly　detection　framework,　called　SFSD,　which　leverages　feature　selection　and　deviation　networks　to　detect　anomalies　in　industrial　control　systems.　Specifically,　we　introduce　a　feature　selection　algorithm　(IG-PCA)　that　utilizes　information　gain　and　principal　component　analysis　to　reduce　the　dimensionality　of　features　in　industrial　control　data　by　eliminating　redundant　features.　Then,　we　propose　a　semi-supervised　learning　method　based　on　an　improved　deviation　network,　which　utilizes　an　anomaly　scoring　network　to　learn　end-to-end　anomaly　scores　for　the　training　data,　thus　assigning　anomaly　scores　to　each　training　data.　Finally,　using　a　limited　amount　of　anomaly-labeled　data,　we　design　a　specific　deviation　loss　function　to　optimize　the　anomaly　scoring　network,　enabling　a　significant　score　bias　between　positive　and　negative　samples.　Experimental　results　demonstrate　that　the　proposed　SFSD　outperforms　existing　semi-supervised　anomaly　detection　frameworks　by　improving　the　accuracy　and　detection　rate　by　an　average　of　1-2%.　Moreover,　SFSD　requires　less　training　time　compared　to　existing　frameworks,　resulting　in　a　training　time　reduction　of　approximately　10%　or　more.