Cognitive Radio Networks (CRNs) have been proposed as a solution to the problem of spectrum scarcity in wireless communication systems. CRNs allow for dynamic spectrum access by secondary users, who can opportunistically use the spectrum when it is not being utilized by primary users. The integration of CRNs with the Internet of Things (IoT) has the potential to bring significant benefits to both wireless communication systems and IoT applications. Spectrum sensing is a crucial component of CRNs, as it enables the secondary users to detect the presence of primary users and opportunistically use the available spectrum. Routing is another important aspect of CRN IOT-based systems, as it enables efficient communication between the devices. Traditional routing protocols may not be suitable for CRN IOT-based systems, due to the dynamic nature of the available spectrum. In this survey paper, we explore the recent research and developments in CRN IOT-based systems, including the architecture, key technologies, and potential applications. Mainly, we will focus on the key aspects of spectrum sensing and routing in CRNs integrated with the IoT. Hence, we review the sensing and routing protocols and discuss their performance in different scenarios. We will also discuss the challenges and future research directions in this field. Overall, this survey aims to provide a comprehensive overview of the state-of-the-art in IOT-based CRNs and to highlight the potential of this technology for addressing the growing demands of wireless communication and IoT applications.

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.

Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.