The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data’s privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users’ privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.

As the number of devices (things) connected to the Internet (Internet of things: IoT) is growing, achieving robust security and privacy (S&P) is becoming increasingly challenging. With the heavy use of medical things (MT), the S&P in the medical domain poses a serious issue that continues to grow. Due to the criticality and sensitivity of the data in the healthcare domain, ensuring the S&P of the Internet of medical things (IoMT) makes matters even more problematic. Lack of proper S&P in IoMT will not only leave patients' privacy at risk but may also put patients' lives at risk. In this paper, we provide a taxonomy of the S&P issues of IoMT. We also provide an approach to quantify IoMT risks and demonstrate how to assess risks in two IoMT devices. This work aims to increase the S&P awareness among IoMT stakeholders by enabling them to identify and quantify potential IoMT S&P risks.

Protecting against interference is essential at a time when wireless communications are essential for sending large amounts of data. Our research presents a novel deep learning technique, the ResNeXt method and embedded Gated Recurrent Unit (GRU) model (RNT), rigorously developed for real-time phishing attack detection. Focused on countering the escalating threat of phishing assaults and bolstering digital forensics, our systematic approach involves SMOTE for managing data imbalance during initial data processing. The model’s discriminative capability is improved, particularly in the feature extraction process, when autoencoders and ResNet (EARN) are integrated with feature engineering. The ensemble technique of feature extraction reveals crucial data patterns. At the core of our AI categorization is the RNT model, optimized using hyperparameters through the Jaya optimization method (RNT-J). Rigorously tested on real phishing attack datasets, our AI model consistently outperforms state-of-the-art algorithms by a substantial margin of 11% to 19% while maintaining exceptional computing efficiency. Furthermore, our model achieves 98% accuracy, low false positive/false negative values, and a statistical execution time with a mean of 36.99s, median of 35.99s, minimum of 34.99s, maximum of 41.99s, and a standard deviation of 1.10s. Moreover, it demonstrates superior accuracy with SMOTE (98%) and without SMOTE (83%) compared to other algorithms. This state-of-the-art AI study, which focuses on digital forensics, offers enhanced security and optimized productivity for businesses and industries, signifying a breakthrough in the continuing battle against phishing attempts. Through strengthening protection against interference in wireless communication, our AI research strives to amplify data accessibility, resilience, and trustworthiness in the face of cybersecurity threats within the organizational context.

In recent years, object detection has gained significant interest and is considered a challenging problem in computer vision. Object detection is mainly employed for several applications, such as instance segmentation, object tracking, image captioning, healthcare, etc. Recent studies have reported that deep learning (DL) models can be employed for effective object detection compared to traditional methods. The rapid urbanization of smart cities necessitates the design of intelligent and automated waste management techniques for effective recycling of waste. In this view, this study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique. The proposed DLSODC-GWM technique mainly focuses on detecting and classifying small garbage waste objects to assist intelligent waste management systems. The DLSODC-GWM technique follows two major processes, namely, object detection and classification. For object detection, an arithmetic optimization algorithm (AOA) with an improved RefineDet (IRD) model is applied, where the hyperparameters of the IRD model are optimally chosen by the AOA. Secondly, the functional link neural network (FLNN) technique was applied for the classification of waste objects into multiple classes. The design of IRD for waste classification and AOA-based hyperparameter tuning demonstrates the novelty of the work. The performance validation of the DLSODC-GWM technique is performed using benchmark datasets, and the experimental results show the promising performance of the DLSODC-GWM method on existing approaches with a maximum $acc{u}_y$ of 98.61%.

Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have certain limitations that might be effectively addressed by leveraging different deep learning architectures. Furthermore, the evaluation of the proposed models is often hindered by imbalanced datasets, limiting a comprehensive assessment of model efficacy. Hence, this study aims to address these challenges by employing data augmentation methods on four prominent datasets, the UNSW-NB15, 5G-NIDD, FLNET2023, and CIC-IDS-2017, to enhance the performance of several deep learning architectures for intrusion detection systems. The experimental results underscored the capability of a simple CNN-based architecture to achieve highly accurate network attack detection, while more complex architectures showed only marginal improvements in performance. The findings highlight how the proposed methods of deep learning-based intrusion detection can be seamlessly integrated into cybersecurity frameworks, enhancing the ability to detect and mitigate sophisticated network attacks. The outcomes of this study have shown that the intrusion detection models have achieved high accuracy (up to 91% for the augmented CIC-IDS-2017 dataset) and are strongly influenced by the quality and quantity of the dataset used.