Internet　of　Things　(IoT)　has　caused　significant　digital　disruption　to　the　future　of　the　digital　world.　With　the　emergence　of　the　5G　technology,　IoT　would　shift　rapidly　from　aspirational　vision　to　real-world　applications.　However,　one　of　the　most　pressing　issues　in　IoT　is　security.　Routing　protocols　of　the　IoT,　such　as　the　Routing　Protocol　for　Low-power　and　lossy　network　protocol　(RPL),　are　vulnerable　to　both　insider　and　outsider　attacks　with　the　insider　ones　being　more　challenging　because　they　are　more　difficult　to　detect　and　mitigate.　Among　the　most　concerning　insider　attacks　to　RPL　in　IoT　applications　is　the　Version　Number　Attacks　(VNAs)　that　target　the　global　repair　mechanisms　by　consuming　resources　of　IoT　devices,　such　as　power,　memory,　and　processing　power,　to　eventually　cause　the　IoT　ecosystem　to　collapse.　In　this　paper,　a　lightweight　VNA　detection　model　named　ML-LGBM　is　proposed.　The　work　on　the　ML-LGBM　model　includes　the　development　of　a　large　VNA　dataset,　a　feature　extraction　method,　an　LGBM　algorithm　and　maximum　parameter　optimization.　Results　of　extensive　experiments　demonstrate　the　advantages　of　the　proposed　ML-LGBM　model　based　on　several　metrics,　such　as　accuracy,　precision,　F-score,　true　negative　rate　and　false-positive　rate　of　99.6%,　99%,　99.6%,　99.3%　and　0.0093,　respectively.　Moreover,　the　proposed　ML-LGBM　model　has　slower　execution　time　and　less　memory　resource　requirement　of　140.217　seconds　and　347,530　bytes,　making　it　suitable　for　resource-constrained　IoT　devices.