In　an　industrial　control　system,　a　programmable　logic　controller　(PLC)　plays　a　vital　role　in　maintaining　the　stable　operation　of　the　system.　Cyber-attacks　can　affect　the　regular　operation　by　tampering　with　the　data　stored　in　the　PLC,　thereby　damaging　to　the　system.　Thus,　it　is　particularly　important　to　develop　an　efficient　cyber-attacks　recovery　method.　However,　owing　to　the　impact　of　unknown　factors　in　theoretical　methods,　poor　scalability　of　automaton　theory,　and　a　lack　of　constraints　during　the　training　process　of　deep　learning　network　models,　the　restoration　accuracy　and　stability　are　low.　Therefore,　it　is　a　significant　challenge　to　design　an　appropriate　method　to　improve　the　accuracy　and　stability　of　cyber-attacks　recovery.　In　this　study,　the　generative　adversarial　networks　were　applied　to　the　problem　of　cyber-attacks　recovery;　furthermore,　a　multi-stage　generative　adversarial　networks　was　designed.　The　model　consisted　of　a　Variational　Autoencoder　and　two　conditional　energy-based　generative　adversarial　networks　(CEBGANs).　Then　the　second　CEBGAN　uses　the　fitted　random　noise　appending　with　the　data　generated　by　the　previous　stage　and　the　historical　data　as　additional　information　to　obtain　the　restoration　results.　Moreover,　a　self-adaptive　decision　policy　was　established　to　enhance　the　restoration　accuracy　and　stability.　Experimental　results　demonstrated　that　the　proposed　method　in　this　manuscript　could　effectively　improve　the　accuracy　of　cyber-attacks　data　recovery　and　reduce　the　possibility　of　outliers　in　data　recovery.