The　Internet　of　Things　(IoT)　has　experienced　significant　growth　in　recent　years　and　has　emerged　as　a　very　dynamic　sector　in　the　worldwide　market.　Being　an　open　-source　platform　with　a　substantial　user　base,　Android　has　not　only　been　a　driving　force　in　the　swift　advancement　of　the　IoT　but　has　also　garnered　attention　from　malicious　actors,　leading　to　malware　attacks.　Given　the　rapid　proliferation　of　Android　malware　in　recent　times,　there　is　an　urgent　requirement　to　introduce　practical　techniques　for　the　detection　of　such　malware.　While　current　machine　learning　-based　Android　malware　detection　approaches　have　shown　promising　results,　the　majority　of　these　methods　demand　extensive　time　and　effort　from　malware　analysts　to　construct　dynamic　or　static　features.　Consequently,　the　practical　application　of　these　methods　becomes　challenging.　Therefore,　this　paper　presents　an　Android　malware　detection　system　characterized　by　its　lightweight　design　and　reliance　on　explainable　machine　-learning　techniques.　The　system　uses　features　extracted　from　mobile　applications　(apps)　to　distinguish　between　malicious　and　benign　apps.　Through　extensive　testing,　it　has　exhibited　exceptional　accuracy　and　an　F1　-score　surpassing　0.99　while　utilizing　minimal　device　resources　and　presenting　negligible　false　positive　and　false　negative　rates.　Furthermore,　the　classifier　model＇s　transparency　and　comprehensibility　are　significantly　augmented　through　the　application　of　Shapley＇s　additive　explanation　scores,　enhancing　the　overall　interpretability　of　the　system.