A　controller　area　network　(CAN)　bus　that　controls　real-time　communication　and　data　transmission　of　electronic　control　units　in　vehicles　lacks　security　mechanisms　and　is　highly　vulnerable　to　attacks.　The　detection　effectiveness　of　existing　In-Vehicle　network　intrusion　detection　systems　(IDSs)　is　usually　reliant　on　training　samples　of　known　attacks.　Owing　to　the　simple　structure　of　the　CAN　bus　protocol,　attackers　can　easily　create　variants　based　on　known　attacks.　In　this　study,　we　propose　a　CAN　bus　IDS　based　on　a　domain　adversarial　neural　network,　which　can　achieve　high　detection　performance　on　unlearned　variant　attack　data.　In　addition,　we　used　feature　fusion　techniques　to　synthetically　extract　the　features　of　the　attack　data　to　improve　detection　capability.　The　experimental　results　demonstrate　that　our　proposed　model　has　high　performance　and　generalization　ability　in　attack　detection.