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With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]