Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew’s Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

Following the recent advances in wireless communication leading to increased Internet of Things (IoT) systems, many security threats are currently ravaging IoT systems, causing harm to information. Considering the vast application areas of IoT systems, ensuring that cyberattacks are holistically detected to avoid harm is paramount. Machine learning (ML) algorithms have demonstrated high capacity in helping to mitigate attacks on IoT devices and other edge systems with reasonable accuracy. However, the dynamics of operation of intruders in IoT networks require more improved IDS models capable of detecting multiple attacks with a higher detection rate and lower computational resource requirement, which is one of the challenges of IoT systems. Many ensemble methods have been used with different ML classifiers, including decision trees and random forests, to propose IDS models for IoT environments. The boosting method is one of the approaches used to design an ensemble classifier. This paper proposes an efficient method for detecting cyberattacks and network intrusions based on boosted ML classifiers. Our proposed model is named BoostedEnML. First, we train six different ML classifiers (DT, RF, ET, LGBM, AD, and XGB) and obtain an ensemble using the stacking method and another with a majority voting approach. Two different datasets containing high-profile attacks, including distributed denial of service (DDoS), denial of service (DoS), botnets, infiltration, web attacks, heartbleed, portscan, and botnets, were used to train, evaluate, and test the IDS model. To ensure that we obtained a holistic and efficient model, we performed data balancing with synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) techniques; after that, we used stratified K-fold to split the data into training, validation, and testing sets. Based on the best two models, we construct our proposed BoostedEnsML model using LightGBM and XGBoost, as the combination of the two classifiers gives a lightweight yet efficient model, which is part of the target of this research. Experimental results show that BoostedEnsML outperformed existing ensemble models in terms of accuracy, precision, recall, F-score, and area under the curve (AUC), reaching 100% in each case on the selected datasets for multiclass classification.

Cloud enterprise data warehousing is a top level strategic business and information technology (IT) investment initiative in any organization that is technologically inclined, profit driven and customer oriented. To build the data warehouse, data are obtained from numerous heterogenous data sources, transformed, cleansed and processed into an applicable data repositories for implementation across the healthcare organizational settings. The current paper constructed an enterprise cloud data warehouse for e-healthcare organization and connected the medical/clinical workforces through the enterprise e-healthcare data warehouse and allowed the medical solutions and clinical information of all the patient to be stored. The proposed system is expected to improved the e-Healthcare information management by providing a model to support medical software automation, hardware system integration and enhances the control and management of the patients records.

In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.