Abstract: The evolution of IoT has brought substantial benefits across several application domains but introduced complex security challenges. Traditional detection systems often struggle to identify attackers more specifically in multiple or integrated attacks. To deal with this problem, this research work proposes an optimized deep fusion network named HydraBoost++ that employs a novel BiConv-FE backbone for feature extraction, MouldSelect in the neck for feature selection, and a boosting head. This overall architecture is inspired by the recent advanced deep learning architectures, and each building block, including backbone, neck, and head, ensures effective handling at each stage of the proposed architecture. The BiConv-FE is mainly designed for the extraction of both local and global context details and to improve classification. However, this integration generates a large amount of information, which is then handled at the neck using the MouldSelect module. The MouldSelect module uses a nature-inspired optimization algorithm and helps in selecting the relevant information for further classification. This stage reduces computational overhead in IoT networks when detection architectures are implemented. The boosting head uses XGBoost as a multi-class classifier and is used to classify the attacker nodes of the network. This proposed HydraBoost++ improves the overall accuracy of the network, which is assessed using publicly available and self-generated datasets. The self-generated dataset consists of labeled simulation traces under different network scenarios and helps in accessing the HydraBoost++ under different network situations. The precision, recall, f-score, and accuracy-based results have shown an incredible contribution of the proposed architecture and have shown better performance than other benchmarked datasets.

Key words: multi-class classification, intruder detection, IoT networks, deep learning, optimization