The　rapid　proliferation　of　Internet　of　Things　(IoT)　devices　has　raised　critical　concerns　regarding　the　security　of　corresponding　IoT　networks.　The　Routing　Protocol　for　Low-Power　and　Lossy　Networks　(RPL),　a　foundational　element　in　IoT　communication,　is　susceptible　to　diverse　routing　attacks　due　to　IoT　nodes＇　constrained　resources　and　open　nature.　This　underscores　the　necessity　for　an　Intrusion　Detection　System　(IDS)　to　safeguard　RPL-based　IoT　networks.　Existing　anomaly-based　IDS　suffer　from　high　false　alarm　rates　(FAR).　In　response　to　these　challenges,　this　paper　presents　the　Ensemble　Learning-based　Intrusion　Detection　System　(ELG-IDS),　which　employs　stacking　and　extreme　parameter　optimization　to　detect　three　RPL　internal　attacks:　version　number,　decreased　rank,　and　DIS　flooding　attacks.　ELG-IDS　employs　enhanced　feature　extraction　and　genetic　algorithm　(GA)-based　feature　selection.　Experimental　results　on　a　dedicated　dataset　demonstrate　ELG-IDS＇s　remarkable　accuracy:　99.18%,　99.38%,　99.66%,　and　97.90%　for　version　number,　rank　attack,　DIS　flooding,　and　an　average　accuracy　of　97.90%　in　multi-classification　mode,　respectively.　This　study　advances　IoT　network　security　through　ELG-IDS,　enhancing　protection　against　evolving　security　challenges.