The Internet of Things (IoT) has a profound technological, physical and economic impact on dayto- day lives. In IoT networks, the interacting nodes are inherently resource-constrained; this would render those nodes to be a source of cyber-attack targets. In this aspect, substantial efforts have been made, mainly through conventional cryptographic methods, to tackle the security and privacy concerns in IoT networks. Yet, the distinctive features of IoT nodes make conventional solutions inadequate to address the IoT network securityspectrum. To cope with these concerns in IoT, a novel Conglomerate Edict based Intrusion Detection System (IDS) is designed in this work. The proposed IDS amalgamates the functioning of several decision based machine learning classifiers to overwhelm the security threats. Detecting an unknown attack seems to be a reverie in IoT security; whereas, the hybrid ensemble discernment classifier in the proposed IDS effectively detects the known as well as unknown attacks with paramount detection rate. Overall, numerous high performancemetrics are evaluated in this work to reveal the proposed efficacy in assuring scalable and secured IoT data transmission.