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The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%.