The　controller　area　network　(CAN)　bus,　which　controls　real-time　communication　and　data　transmission　among　vehicle　electronic　control　units,　lacks　security　mechanisms　and　is　highly　vulnerable　to　attacks.　Existing　in-　vehicle　network　intrusion　detection　systems　(IDSs)　typically　rely　on　deep　learning　models　for　detection,　which　are　susceptible　to　interference　from　adversarial　attacks　owing　to　the　vulnerability　of　the　models　themselves,　thereby　compromising　the　detection　performance.　In　this　study,　we　propose　an　adversarial　attack　detection　method　based　on　gradient　correlation　that　achieves　a　high　accuracy　rate　using　a　linear　approach　to　detect　adversarial　samples.　The　experimental　results　show　that　the　proposed　model　does　not　require　retraining　of　the　original　detection　model　and　demonstrates　better　detection　performance　for　multiple　adversarial　attacks.