Internet　of　Things　(IoT)　cyber-attacks　are　growing　day　by　day　because　of　the　constrained　nature　of　the　IoT　devices　and　the　lack　of　effeective　security　countermeasures.　These　attacks　have　small　variants　in　their　behavior　and　properties,　implying　that　the　traditional　solutions　cannot　detect　the　small　mutant　variations.　Therefore,　a　robust　detection　method　becomes　necessary.　One　of　the　common　attacks　is　routing　protocol　for　low　power　and　lossy　network　attacks,　which　has　not　been　well　investigated　in　the　literature.　In　this　paper,　we　propose　an　artificial　neural　network　(ANN)　model　for　detecting　decreased　rank　attacks,　which　includes　three　phases:　Data　pre-processing,　Feature　extraction　using　random　forest　classifier,　and　an　artificial　neural　network　model　for　the　detection.　The　proposed　model　has　been　tested　in　multi　and　binary　detection　scenarios　using　the　IRAD　dataset.　The　results　obtained　are　promising　with　accuracy,　precision,　falsepositive　rate,　and　AUC-ROC　scores　of　97.14%,　97.03%,　0.36%,　and　98%,　respectively.　The　proposed　approach　is　efficient　and　outperforms　previous　methods　of　precision,　recall,　and　F-score　metrics.　©　2021.　All　Rights　Reserved