Implementing IoT into core infrastructures has led to an increasing trend in security challenges, noticeably black hole attacks, which present a risk to the integrity of network communications. Ever-growing applications in ground-level services underpin the need to ensure full network integrity. In this respect, driven by this urgent real need, the research has elaborated an enhanced detection framework able to effectively detect black hole attacks occurring in IoT networks using the RPL protocol. The approach uses the rich pattern recognition capabilities of a deep MLP model, which was thoroughly trained and validated using a dataset generated in simulated scenarios in Cooja. The most important achievements of this work are that the general model accuracy was 94.3%, while the specific accuracies to the training dataset, the validation dataset, and the testing dataset are 94.4%, 94.2%, and 94%, respectively. Besides, the model yielded very small Mean Squared Error values; the minimum validated MSE was about 0.029192. However, it was further characterized by performance with ROC very close to perfection, bearing AUC values very close to 1 in both classes for all datasets. These performance metrics will validate the model’s efficacy in deciphering the subtleties of black hole attacks, hence enhancing the network security analytics and contributing a great deal toward proactive defense mechanisms against cyber threats in IoT networks. The results of this research on the deep learning model for cybersecurity show, at the same time, innovative approaches that can be applied in the protection of the ever-growingly complex IoT infrastructures.