Controller　area　network　(CAN)　bus　anomaly　detection　on　the　Internet　of　Vehicles　(IoV)　is　a　topic　of　ongoing　interest　and　increasing　importance,　particularly　as　IoV　becomes　commonplace.　However,　existing　abnormal　detection　schemes　are　not　ideal　due　to　the　lack　of　abnormal　data　in　IoV　and　the　difficulty　of　parsing　the　diverse　message　rules　in　existing　vehicles.　While　single-classification　of　the　support　vector　domain　description　(SVDD)　algorithm　can　detect　abnormalities　only　with　normal　message　information,　the　approach　has　a　high　rate　of　false　negatives　when　deployed　directly　in　the　in-vehicle　network　environment.　In　addition,　the　real-time　data　generation　(e.g.,　IoV　messages)　compounds　the　challenge　of　designing　effective　detection　approaches.　Therefore,　this　article　proposes　a　mechanism　for　car　networking　message　classification　for　a　broad　range　of　data,　and　establishes　a　weak　model　for　many　simple　redundant　data　in　the　vehicle　Intranet.　The　detection　can　reduce　the　time　and　computation　costs　of　detection　while　ensuring　accuracy.　Then,　this　article　proposes　two　improved　SVDD　schemes:　1)　M-SVDD　scheme,　which　adds　the　Markov　chain　to　detect　time-related　messages　and　2)　G-SVDD　scheme,　which　maps　the　kernel　function　to　the　Gaussian　kernel　function　to　reduce　the　redundant　area　of　the　model　and　the　false-negative　rate.　The　experimental　results　show　that　our　proposed　schemes　have　higher　accuracy,　recall　rate,　and　fewer　computation　overhead.