Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning on Big data hinders the scalability of IIoT intrusion detection systems. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-lev
Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common featur
A fascinating new preprint reports the results of examining the VOC composition from individual cell cultures derived from human oral cancers, using an experimental setup employing an insect brain with attached antenna.