In this paper, we propose an optimized lightweight Federated Deep Learning (FDL) method for botnet attack detection in smart critical infrastructure. First, an optimization method is developed to determine the most appropriate combination of model hyperparameters for local Deep Learning (DL) at the edge nodes. Then, an oversampling algorithm is combined with the optimal DL model to improve the classification performance when the training data is highly imbalanced, without a significant increase in the overall computation time. Furthermore, a feature dimensionality reduction method is used to reduce the amount of memory space required to store the network traffic data at the edge nodes.

Deep Learning (DL) models can be trained to automatically learn the underlying features of the traffic patterns in IoT networks to detect complex botnet attacks. However, the performance of a neural network model largely depends on the set of hyperparameters that is used for the model development. In this paper, an algorithm is proposed to determine the optimal set of hyperparameters (the numbers of hidden layers and hidden units, the learning rate, the optimiser, the activation function, the batch size, and the number of epochs) for efficient DL-based botnet detection in IoT networks. The DL models employ a Deep Neural Network (DNN) architecture for binary and multi-class classification. DNN-based botnet detection models are developed and experiments are performed with the Bot-IoT and N-BaIoT datasets to validate the effectiveness of the hyperparameter optimisation method. Experiment results showed that the proposed method produced DNN models that achieved high botnet attack detection rates, low false alarm rates, and near real-time computation speed.

The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.