Vulnerability　mining　technology　is　used　for　protecting　the　security　of　industrial　control　systems　and　their　network　protocols.　Traditionally,　vulnerability　mining　methods　have　the　shortcomings　of　poor　vulnerability　mining　ability　and　low　reception　rate.　In　this　study,　a　test　case　generation　model　for　vulnerability　mining　of　the　Modbus　TCP　based　on　an　anti-sample　algorithm　is　proposed.　Firstly,　a　recurrent　neural　network　is　trained　to　learn　the　semantics　of　the　protocol　data　unit.　The　softmax　function　is　used　to　express　the　probability　distribution　of　data　values.　Next,　the　random　variable　threshold　and　the　maximum　probability　are　compared　in　the　algorithm　to　determine　whether　to　replace　the　current　data　value　with　the　minimum　probability　data　value.　Finally,　the　Modbus　application　protocol　(MBAP)　header　is　completed　according　to　the　protocol　specification.　Experiments　using　the　anti-sample　fuzzer　show　that　it　not　only　improves　the　reception　rate　of　test　cases　and　the　ability　to　exploit　vulnerabilities,　but　also　detects　vulnerabilities　of　industrial　control　protocols　more　quickly.