Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system’s effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.

Wearable antennas have grown in popularity in recent years as a result of their appealing features and prospects to actualize lightweight, compact, low-cost and adaptable wireless communications and surroundings. These antennas have to be conformal and made of lightweight materials in a low-profile arrangement when attached to various parts of the human body. Near-body operation of these antennas should be possible without degradation. When these characteristics are taken into account, the layout of wearable antennas become challenging, especially when textile substrates are investigated, high conductivity materials are used during manufacturing procedures and body binding scenarios have an impact on the design’s performance. Several of these issues arise in the context of body-worn deployment, despite modest changes in magnitude between implementations. This paper examines the multiple issues and obstacles encountered in the construction of wearable antennas as well as the range of materials used, and the Specific Absorption Rate (SAR) effects employed as well as the bending scheme. An overview of the innovative features and their separate approaches to addressing the difficulties lately raised by work in this field conducted by the scientific community is provided as an appendix.

Microwave (MW) and millimeter-wave (mmW) propagation are severely affected by dust storms and sand storms in arid and semi-arid areas. Electromagnetic waves may suffer from attenuation due to suspended particles during a dust storm. This paper proposes an empirical model to predict the attenuation due to dust storms based on a one-year measurement of visibility, humidity and their effects on MW links in Sudan. Signal strength variations on two operational MW links at 14 and 22 GHz as well as visibility were monitored simultaneously. The model is developed empirically using measured attenuation and measured storm characteristics (e.g., visibility, dielectric constant, frequency, and moisture content). The predicted attenuation from the proposed empirical model is compared with the attenuation at frequencies ranging from 7.5 to 40 GHz measured at different locations, and good agreement is found. Additionally, this method is characterized by simplicity and capability to predict reliable dust storm attenuation for a wide range of frequencies and moisture levels.

Supervisory Control and Data Acquisition (SCADA) systems are crucial for modern industrial processes and securing them against increasing cyber threats is a significant challenge. This study presents an advanced method for bolstering SCADA security by employing a modified hybrid deep learning model. A key innovation in this work is integrating the Self-similarity Hurst parameter into the dataset alongside a CNN-LSTM model, significantly boosting the Intrusion Detection System’s (IDS) capabilities. The Hurst parameter, which quantifies the self-similarity in a dataset, is instrumental in detecting anomalies. Our in-depth analysis of the CICIDS2017 dataset sheds light on contemporary attack patterns and network traffic behaviors. The CNN-LSTM architecture was substantially altered by adding multiple convolutional layers with progressively increasing filters, batch normalization for stable training, and dropout layers for regularization. Principal Component Analysis (PCA) was applied for dimensionality reduction, thereby optimizing the dataset. Test results demonstrate the superior performance of the model incorporating the Hurst parameter, achieving 95.21% accuracy and 82.59% recall, significantly surpassing the standard model. The inclusion of the Hurst parameter marks a substantial advancement in identifying emerging threats, while architectural improvements to the CNN-LSTM model led to more robust and accurate intrusion detection in industrial control settings.

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. The objective of this research is to develop a Patient-Centered Blockchain-Based EHR Management (PCEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure by means of granting or revoking access or viewing one’s records. We used an Ethereum blockchain and IPFS (inter-planetary file system) to store records because of its advantage of being distributed and ensuring the immutability of records and allowing for the decentralized storage of medical metadata, such as medical reports. To achieve secure a distributed, and trustworthy access control policy, we proposed an Ethereum smart contract termed the patient-centric access control protocol. We demonstrate how the PCEHRM system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner and integrate utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions. Finally, we tested the proposed framework in the Windows environment by compiling a smart contract prototype using Truffle and deploy on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The findings of the study indicate that the proposed strategy is both efficient and practicable.

Consensus protocols stand behind the success of blockchain technology. This is because parties that distrust each other can make secure transactions without the oversight of a central authority. The first consensus protocol emerged with Bitcoin. Since then, many others have appeared. Some of them have been implemented by official blockchain platforms, whereas others, for the time being, remain as proposals. A blockchain consensus is a trade-off. The new solutions promise to overcome the known drawbacks of blockchain, but they may also bring new vulnerabilities. Moreover, blockchain performance metrics are not clearly defined, as some metrics, such as delay and throughput, which are key factors for the efficiency of standard networks, are purposely constrained by most mainstream blockchain platforms. The main body of this paper consolidates knowledge of blockchains, focusing on the seminal consensus protocols in large-scale market capitalization platforms, and how consensus is achieved for large-scale, decentralized, blockchain architectures. The benefits, limitations, and tradeoffs, as well as the subsequent trend in current consensus development, and its limitations as a general paradigm, are highlighted. The paper also sheds light on overlooked potential performance metrics, and it proposes some novel solutions to some of the identified problems.