Security issues are still challenging due to the availability of brilliant skills and hacking tools. Thus, detecting the intrusion in the IoT environment is crucial. Hence, this research introduces a novel optimised deep residual network based on the trust and KDD parameters. Here, an efficient mayfly spider monkey optimisation (MSMO) is proposed for tuning the adjustable parameters of the intrusion detector named deep residual network (DRN), which is modelled by hybridising the social behaviour of the mayfly in the mayfly optimisation algorithm (MA) with the foraging behaviour of the spider monkey based on the fission property of the spider monkey optimisation (SMO) to obtain the global best solution. Here, the trust factors and the KDD Cup features are considered for learning the classifier. The proposed model obtained better performance in accuracy of 0.913, precision of 0.919, false alarm rate of 0.084, and recall of 0.958.