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The Internet of Things (IoT) has become a popular computing technology paradigm. It is increasingly being utilized to facilitate human life processes through a variety of applications, including smart healthcare, smart grids, smart finance, and smart cities. Scalability, interoperability, security, and privacy, as well as trustworthiness, are all issues that IoT applications face. Blockchain solutions have recently been created to help overcome these difficulties. The purpose of this paper is to provide a survey and tutorial on the use of blockchain in IoT systems. The importance of blockchain technology in terms of features and benefits for constituents of IoT applications is discussed. We propose a blockchain taxonomy for IoT applications based on the most significant factors. In addition, we examine the most widely used blockchain platforms for IoT applications. Furthermore, we discuss how blockchain technology can be used to broaden the spectrum of IoT applications. Besides, we discuss the recent advances and solutions offered for IoT environments. Finally, we discuss the challenges and future research directions of the use of blockchain for the IoT.

Network Intrusion Detection Systems (NIDS) are designed to safeguard the security needs of enterprise networks against cyber-attacks. However, NIDS networks suffer from several limitations, such as generating a high volume of low-quality alerts. Moreover, 99% of the alerts produced by NIDSs are false positives. As well, the prediction of future actions of an attacker is one of the most important goals here. The study has reviewed the state-of-the-art cyber-attack prediction based on NIDS Intrusion Alert, its models, and limitations. The taxonomy of intrusion alert correlation (AC) is introduced, which includes similarity-based, statistical-based, knowledge-based, and hybrid-based approaches. Moreover, the classification of alert correlation components was also introduced. Alert Correlation Datasets and future research directions are highlighted. The AC receives raw alerts to identify the association between different alerts, linking each alert to its related contextual information and predicting a forthcoming alert/attack. It provides a timely, concise, and high-level view of the network security situation. This review can serve as a benchmark for researchers and industries for Network Intrusion Detection Systems’ future progress and development.

Data stream mining deals with processing large amounts of data in nonstationary environments, where the relationship between the data and the labels often changes. Such dynamic relationships make it difficult to design a computationally efficient data stream processing algorithm that is also adaptable to the nonstationarity of the environment. To make the algorithm adaptable to the nonstationarity of the environment, concept drift detectors are attached to detect the changes in the environment by monitoring the error rates and adapting to the environment’s current state. Unfortunately, current approaches to adapt to environmental changes assume that the data stream is fully labeled. Assuming a fully labeled data stream is a flawed assumption as the labeling effort would be too impractical due to the rapid arrival and volume of the data. To address this issue, this study proposes to detect concept drift by anticipating a possible change in the true label in the high confidence prediction region. This study also proposes an ensemble-based concept drift adaptation approach that transfers reliable classifiers to the new concept. The significance of our proposed approach compared to the current baselines is that our approach does not use a performance measur as the drift signal or assume a change in data distribution when concept drift occurs. As a result, our proposed approach can detect concept drift when labeled data are scarce, even when the data distribution remains static. Based on the results, this proposed approach can detect concept drifts and fully supervised data stream mining approaches and performs well on mixed-severity concept drift datasets.

BackgroundThe ongoing armed conflict in Sudan, which began on April 15, 2023, has severely impacted the nation's health care system, exacerbating existing vulnerabilities and creating new public health challenges. Objective: This rapid review aims to assess the impact of this conflict on health services and evaluate the health consequences for Sudan's population.MethodsA rapid review protocol guided the search and defined the inclusion and exclusion criteria. Electronic databases (PubMed, MEDLINE, Google Scholar, and Global Health) were searched for relevant articles from April 2023 to November 2024. Articles focusing on the impact of armed conflict on health care services were included. Data extraction and narrative synthesis were used to report the findings. Results: Thirty articles met the inclusion criteria. Key findings include the closure of 70% of healthcare facilities in war-affected states, severe shortages of medical supplies, and significant disruptions in essential health services. This conflict has increased the risk of infectious diseases, malnutrition, and mental health issues. Violations of humanitarian corridors have impeded aid delivery, and the healthcare financing system is strained by reduced government contributions and rising out-of-pocket expenses. The humanitarian crisis has led to increased displacement, with over six million internally displaced persons and 1.57 million refugees in neighboring countries. ConclusionThe conflict in Sudan has critically undermined the healthcare system, resulting in severe public health challenges. There is an urgent need for coordinated international intervention to strengthen health systems, ensure essential services, and address the root causes of conflict. Sustainable solutions must focus on immediate relief and long-term resilience, leveraging lessons from other conflict-affected regions to support Sudan’s healthcare recovery and reform.