Internet of Things (IoT) devices have increased the complexity of network environments and exposed them to various cyber-attacks, for maintaining network security. However, Intrusion Detection (ID) techniques often struggle in handling high-dimensional network traffic data, identifying the most relevant features. To overcome these challenges, this paper proposes a Honey Badger Algorithm-Based Hyperparameter Optimization of a Multi-Head Attention Network (HBA-MHAN) for IoT-ID. Data pre-processing based on data cleaning and label encoding used to improve the quality then stability of the input data. Data visualization is used to analyse the features in addition distribution of the network traffic. Subsequently, Recursive Feature Elimination (RFE) is applied to select the most appropriate features and eliminate redundant informative attributes. The selected features are delivered to the MHAN, which learns important relationships among network traffic features through multiple attention heads. The HBA is employed to optimize the key hyperparameters of the MHAN, improving the learning process and ID performance. The optimized MHAN performs ID and classifies network traffic into the corresponding attack or normal categories. Experimental evaluation on the TON_IoT network dataset using Python demonstrates that the proposed HBA-MHAN achieves 95% accuracy, 96% precision, 98.55% recall, and 97% F1-score for IoT-ID. The proposed structure improves ID performance whereas reducing the impact of irrelevant and redundant network features for efficient and accurate IoT-ID.