The　Internet　of　Things　(IoT)　is　evolving　rapidly,　increasing　demand　for　safeguarding　data　against　routing　attacks.　While　achieving　complete　security　for　RPL　protocols　remains　an　ongoing　challenge,　this　paper　introduces　an　innovative　hybrid　autoencoder-decision　tree　framework　(HADTF)　designed　to　detect　four　types　of　RPL　attacks:　decreased　rank,　version　number,　DIS　flooding,　and　blackhole　attacks.　The　HADTF　comprises　three　key　components:　enhanced　feature　extraction,　feature　selection,　and　a　hybrid　autoencoder-decision　tree　classifier.　The　enhanced　feature　extraction　module　identifies　the　most　pertinent　features　from　the　raw　data　collected,　while　the　feature　selection　component　carefully　curates＇　optimal　features　to　reduce　dimensionality.　The　hybrid　autoencoder-decision　tree　classifier　synergizes　the　strengths　of　both　techniques,　resulting　in　high　accuracy　and　detection　rates　while　effectively　minimizing　false　positives　and　false　negatives.　To　assess　the　effectiveness　of　the　HADTF,　we　conducted　evaluations　using　a　self-generated　dataset.　The　results　demonstrate　impressive　performance　with　an　accuracy　of　97.41%,　precision　of　97%,　recall　of　97%,　and　F1-score　of　97%.　These　findings　underscore　the　potential　of　the　HADTF　as　a　promising　solution　for　detecting　RPL　attacks　within　IoT　networks.