The　deep　integration　of　physical　devices　and　communication　networks　has　increased　the　security　risks　of　cyber-physical　systems　(CPSs)　compared　to　traditional　control　systems.　Deep　learning-based　intrusion　detection　systems　(IDSs)　play　a　crucial　role　in　ensuring　CPSs　security.　However,　the　existing　IDSs　often　rely　on　known　attack　features,　rendering　them　unable　to　withstand　emerging　new　attacks　arising　from　the　dynamic　evolution　of　intrusion　behaviors.　This　paper　aims　to　develop　an　IDSs　with　high　adaptability　and　strong　generalization　capabilities,　which　is　capable　of　rapidly　adapting　to　new　attack　classes　with　only　a　few　new　samples.　To　achieve　this　objective,　we　propose　CAT-IDS,　a　few-shot　class-incremental　adaptation　strategy　for　an　IDS　to　counteract　new　attacks　on　CPSs.　We　design　a　highly　symmetric　classifier　structure　for　CAT-IDS　that　can　flexibly　adjust　the　classification　space　to　adapt　to　new　attacks.　Furthermore,　we　calibrate　the　biased　distribution　formed　by　a　few　training　samples　through　statistical　feature　transfer.　In　order　to　prevent　the　model　from　forgetting　old　attack　information　during　the　adaptation　process,　we　devise　hybrid　features　for　attack　detection.　These　features　contain　essential　information　for　both　old　and　new　class　classifications.　We　demonstrate　the　effectiveness　of　CAT-IDS　through　multiple　experiments　on　three　CPSs　datasets.　The　results　show　that　CAT-IDS　achieves　an　average　accuracy　improvement　of　approximately　4.　5%　compared　to　the　state-of-the-art　methods,　demonstrating　its　superior　ability　to　adapt　to　new　attacks　while　maintaining　high　performance　in　classifying　existing　attacks.　　©　2004-2012　IEEE.