The　astonishing　growth　of　sophisticated　ever-evolving　cyber　threats　and　attacks　throws　the　entire　Internet-of-Things　(IoT)　infrastructure　into　chaos.　As　the　IoT　belongs　to　the　infrastructure　of　interconnected　devices,　it　brings　along　significant　security　challenges.　Cyber　threat　analysis　is　an　augmentation　of　a　network　security　infrastructure　that　primarily　emphasizes　on　detection　and　prevention　of　sophisticated　network-based　threats　and　attacks.　Moreover,　it　requires　the　security　of　network　by　investigation　and　classification　of　malicious　activities.　In　this　study,　we　propose　a　DL-enabled　malware　detection　scheme　using　a　hybrid　technique　based　on　the　combination　of　a　Deep　Neural　Network(DNN)　and　Long　Short-Term　Memory(LSTM)　for　the　efficient　identification　of　multi-class　malware　families　in　IoT　infrastructure.　The　proposed　scheme　utilizes　latest　2018　dataset　named　as　N_BaIoT.　Furthermore,　our　proposed　scheme　is　evaluated　using　standard　performance　metrics　such　as　accuracy,　recall,　precision,　F1-score,　and　so　forth.　The　DL-based　malware　detection　system　achieves　99.96%　detection　accuracy　for　IoT　based　threats.　Finally,　we　also　compare　our　proposed　work　with　other　robust　and　state-of-the-art　detection　schemes.