The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) features. We created a novel dataset by scraping over 13,000 mobile phone reviews from Flipkart’s website. The proposed approach involves the development of a federated deep neural network (FDNN) to classify the newly created Flipkart dataset. This approach includes data cleaning, balancing, TF-IDF feature extraction, and prediction using federated learning. We employ two clients and one server and conduct three rounds of experiments. The experimental results demonstrate that the proposed approach achieved an accuracy of 96.68% on the aggregated server side while maintaining the security of customer data on their local devices. The proposed approach has the potential to assist consumers in making well-informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.

Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies to increase detection and classification performance without having to develop the training models from scratch. It adopts the fusion of 3 CNNs, ResNet18, MobileNetV2, and DenseNet161, by using the random forest voting strategy. Experiments are carried out using a publicly available dataset, MaleVis, to assess and validate the suggested approach. MaleVis contains 14,226 RGB converted images representing 25 malware classes and one benign class. The obtained findings show that our suggested approach outperforms the existing state-of-the-art solutions in terms of detection and classification performance; it achieves a precision of 98.74%, recall of 98.67%, a specificity of 98.79%, F1-score of 98.70%, MCC of 98.65%, an accuracy of 98.68%, and an average processing time per malware classification of 672 ms.