The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT) concepts in industrial sectors and applications. IIoT has several applications in smart homes, smart cities, smart grids, connected cars, and supply chain management. However, these systems are being more frequently targeted by cybercriminals. Deep learning and big data analytics have great potential in designing and developing robust security mechanisms for IIoT networks. In this paper, a novel hybrid deep random neural network (HDRaNN) for cyberattack detection in the IIoT is presented. The HDRaNN combines a deep random neural network and a multilayer perceptron with dropout regularization. The proposed technique is evaluated using two IIoT security-related datasets: (i) DS2OS and (ii) UNSW-NB15. The performance of the proposed scheme is analyzed through a number of performance metrics such as accuracy, precision, recall, F1 score, log loss, Region of Convergence (ROC), and Area Under the Curve (AUC). The HDRaNN classified 16 different types of cyberattacks using with higher accuracy of 98% and 99% for DS2OS and UNSW-NB15, respectively. To measure the effectiveness of the proposed scheme, the performance metrics are also compared with several state-of-the-art attack detection algorithms. The findings of HDRaNN proved its superior performance over other DL-based schemes. The deployment perspective of the proposed work is also highlighted in this work.

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The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.