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The remote direct memory access (RDMA) technology, which has been effectively utilized to enhance data transfer rates and reduce CPU utilization, has played a significant role in various domains such as cross-regional data center transfers, high-performance computing, and rapid data read/write operations. However, despite its emerging status, RDMA has been lacking in widely recognized security solutions. With the expansion of RDMA applications from dedicated network environments to general Ethernet networks, the need to address the security risks faced by these applications has become imperative. A set of protective measures were established to ensure security without compromising transmission efficiency. Furthermore, the unique underlying implementation and protocol design of RDMA technology have resulted in incompatibility with existing mature security solutions and have led to the confrontation with specific security risks. The development of RDMA attack and defense technologies and the provision of security guarantees for the forthcoming widespread application of RDMA technology were elucidated The principles of RDMA technology and its implementation were proposed, with examples drawn from the InfiniBand (IB) and the RDMA over converged Ethernet (RoCE) second edition protocols. The security risks faced by RDMA applications in relevant scenarios were investigated, and a summary of the research progress in the RDMA security field over recent years was provided. Effective security solutions addressing these risks were compiled and, after their defensive capabilities were proven, a comparison of their advantages and disadvantages was conducted through theoretical analysis and experimental data. Finally, improvement plans and prospects for technological optimization in the RDMA attack and defense field were proposed.

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc. We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed. [ABSTRACT FROM AUTHOR]

In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.

Large data volumes make manually keeping and preserving health records for future reference problematic. Most unusual is the coronavirus epidemic's overcrowding of hospitals. The Secure Pattern Electronic Healthcare Records (SPEHR) scheme provides a powerful and lightweight secure storage system for cloud-based Ehealthcare services that meets remote healthcare security criteria. So we proposed a neutrosophic model with a multicriterion decision-making (MCDM) method for the analysis and evaluation of these criteria. The neutrosophic model is used to deal with vague and uncertain data. Then we integrated the neutrosophic model with the AHP method to rank and compute the weights of the criteria. Offering stakeholders a secure interface and avoiding unwanted a ccess to cloud-stored data mitigates E-healthcare risks. The key derivation ensures end -to-end data. ciphering to prevent unauthorized use (KDF). This work provides robust security solutions for various environments. This work can meet security needs for people and secure-communicating organizations. We found the data privacy is the best criterion. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons. [ABSTRACT FROM AUTHOR]