The management of contemporary communication networks of Supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things (IoT) in the legacy industry networks, such as Cyber-Physical Systems (CPS). Hence, in this case, it becomes a challenging task to secure networks of SC 4.0 from cyber-attacks and provide a robust and efficient defence framework that can resist sophisticated attacks. Classical machine learning approaches function on the legacy group of data entirely placed on a central or single server, which brands it the least favored choice for Supply chain networks of CPSs with data privacy issues. To address these problems, the authors propose a federated learning-based efficient detection model named, DFF-SC4N, to proactively identify intrusions from SC networks of IoT-driven CPSs using distributed local data training. The DFF-SC4N model uses communication rounds in a federated learning manner having Gated Recurrent Units (GRUs) by only sharing the learned parameters and keeps the data intact on local servers. The accuracy of the global model is optimized by aggregating model updates from multiple servers, which allows multiple SC networks of CPS firms to cooperatively form a comprehensive security model. Extensive experiments on real industrial network data demonstrate that the DFF-SC4N outperforms both centralized training models and state-of-the-art peer methods in protecting SC networks.

The Internet of Things (IoT) is an emerging technology that allow smart devices to communicate through various heterogeneous channels (wired or wireless). However, for conventional networks, it has become a challenging task to efficiently control and manage the data flows of a huge number of devices. Software-defined networking (SDN) is a new way of thinking about networking. Because it is programmable, flexible, agile, and gives you a big picture of the network, it has tried to solve some IoT problems, like scalability, heterogeneity, and complexity. In large-scale SDN-IoT networks, there is a requirement for routing protocols that are both efficient and secure in order to ensure a superior level of quality of service (QoS) and quality of experience (QoE). To address the above stated challenges, a novel deep reinforcement learning (DRL) known as DQQS model is proposed. The aim is to achieve QoS and QoE while also ensuring the security of the SDN-IoT network. The proposed DQQS model dynamically extracts patterns from the past network history by interacting with the underlying network and generating optimized routing policies. This article employs three network metrics—throughput, latency, and the probability of avoiding malicious nodes—to measure the performance of DQQS. Simulations reveal that the proposed framework outperforms four state-of-the-art routing algorithms: OSPF, L-L Routing, Sailfish Routing, and RL-Routing in terms of both throughput and latency. For instance, in an attacked environment, the proposed DQQS model achieved the highest throughput value of 14.5 Mbps, surpassing OSPF at 8 Mbps, L-L at 8.2 Mbps, Sailfish at 9 Mbps, and RL at 9.5 Mbps. Similarly, this model exhibited superior performance in latency, recording the lowest latency value of 52 ms, compared to OSPF 88 ms, L-L 85 ms, Sailfish 72 ms, and RL 75 ms routing algorithms. The experimental results demonstrate that this new DQQS model is a pioneering deep reinforcement learning-based technique that optimally addresses secure routing in the SDN-IoT environment, ensuring enhanced quality of service and experience, and outperforming state-of-the art DL methodologies in both security and network performance metrics.

The convergence of the Internet of Things (IoT) and software-defined networking (SDN) has paved the way for a new technological paradigm in Healthcare Industry 5.0. This integration addresses the complexity, heterogeneity, and dynamic nature of smart IoT devices within healthcare systems. However, it also increases the risk of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks, which pose significant threats to such critical infrastructure. While deep learning (DL)-based intrusion detection methods have demonstrated high accuracy in detecting these attacks, their opaque decision-making process often leads to their characterization as black-box models, limiting their practical use for security analysts. To overcome these challenges, this study proposes an explainable hybrid model for DDoS attack detection in SDN-IoT-based Healthcare Industry 5.0 environments. The model combines the strengths of convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks for capturing temporal dependencies in network traffic. Implemented using an SDN controller, the model accurately classifies DDoS and IoT attacks while providing transparency through the shapley additive explanation (SHAP) method, which identifies the most influential features in the model’s decision-making process. Simulation results on the CICDDoS2019 and IoT Healthcare Security datasets demonstrate the model’s effectiveness, achieving detection accuracy of 99.59% and 98.12%, respectively. These findings confirm the robustness of the proposed hybrid model compared to state-of-the-art methods for detecting potential attacks in Healthcare Industry 5.0 systems.

The Internet of Medical Things (IoMT) has become a novel paradigm for real-time healthcare applications. Artificial intelligence (AI) based efforts have been made to address the security challenges of IoMT, the problem of imbalance data still exists, due to which AI algorithms cannot sufficiently learn malicious traffic behavior and fail to identify rare anomalies in imbalance data accurately. Therefore, in this article, we propose an intelligent model based on software defined networking and deep learning (DL) to handle the heterogeneous, complex, and distributed architecture. To tackle the imbalance challenge, the proposed model utilizes generative adversarial network (GAN) to generate plausible synthetic data for minor class traffic. It combines autoencoder-driven DL models with reconstruction error and Wasserstein distance-based GAN. When compared to naive and advanced techniques, the proposed model produced noticeably better results on an imbalance dataset and outperformed these techniques by 4.78% and 4.54% in terms of accuracy and F1-score values, respectively.

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice.

Background: Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are well-recognized conditions resulting from high blood pressure, rising blood glucose, and elevated eye pressure. Later-stage symptoms usually include patches of cotton wool, restricted veins in the optic nerve, and buildup of blood in the optic nerve. Severe consequences include damage of the visual nerve, and retinal artery obstruction, and possible blindness may result from these conditions. An early illness diagnosis is made easier by the use of deep learning models and artificial intelligence (AI). Objectives: This study introduces a novel methodology called CAD-EYE for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues. Methods: The proposed system combines the features extracted using two deep learning (DL) models (MobileNet and EfficientNet) using feature fusion to increase the diagnostic system efficiency. The system uses fluorescence imaging for increasing accuracy as an image processing algorithm. The algorithm is added to increase the interpretability and explainability of the CAD-EYE system. This algorithm was not used in such an application in the previous literature to the best of the authors’ knowledge. The study utilizes datasets sourced from reputable internet platforms to train the proposed system. Results: The system was trained on 65,871 fundus images from the collected datasets, achieving a 98% classification accuracy. A comparative analysis demonstrates that CAD-EYE surpasses cutting-edge models such as ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception in terms of classification accuracy. A state-of-the-art comparison shows the superior performance of the model against previous work in the literature. Conclusions: These findings support the usefulness of CAD-EYE as a diagnosis tool that can help medical professionals diagnose an eye disease. However, this tool will not be replacing optometrists.