Mesopotamian Journal of Computer Science

Computer science research is a rapidly evolving field that is shaping the future of technology. From artificial intelligence
and machine learning to image processing and cybersecurity, researchers are constantly pushing the boundaries of what is
possible. In this editorial, we will explore some of the current trends in computer science research and their potential impact
on society. After searching the most prestigious academic databases, we settle on a list of subjects widely recognized as
cutting-edge in the field of computer science.

As the Internet of Things (IoT) continues to grow, the security concerns associated with this technology
become increasingly important. Blockchain and Distributed Ledger Technologies (DLTs) are emerging
as potential solutions for IoT security due to their decentralized and immutable nature. In this survey
paper, we provide a comprehensive overview of the state-of-the-art research in the use of blockchain and
DLTs for IoT security. We begin by introducing the basic concepts of blockchain and DLTs, followed
by a discussion of their potential applications in IoT security. We then review the existing literature on
the topic, including both academic and industrial research. We categorize the literature into different
application areas, such as authentication, access control, and data integrity. Finally, we discuss the open
research challenges and potential future directions for the use of blockchain and DLTs in IoT security.
This survey paper provides a valuable resource for researchers and practitioners interested in
understanding the current state-of-the-art research in this important area of IoT security.

This research paper explores the concept of Decentralized Finance (DeFi) and the importance of
governance and decision-making in DeFi platforms. The paper reviews the literature on traditional
finance governance models and blockchain-based governance mechanisms in DeFi platforms, and
discusses the challenges and opportunities in designing decentralized governance models for DeFi
platforms. The research question is "What are the challenges and opportunities in designing decentralized
governance models for DeFi platforms?" and the study objectives are to review the literature on
traditional finance governance models, analyze blockchain-based governance mechanisms in DeFi
platforms, and discuss the challenges and opportunities in designing decentralized governance models
for DeFi platforms. A mixed-methods approach is utilized, combining a systematic review of the
literature with a survey of DeFi platform users. The results reveal that there is a growing interest in
decentralized governance mechanisms among DeFi platform users, but there are also several challenges
that need to be addressed in designing effective decentralized governance models. This research
contributes to the ongoing discussion on the importance of governance and decision-making in DeFi
platforms, and provides insights for the design and implementation of decentralized governance
mechanisms in the DeFi ecosystem.

With the increasing use of encryption in network traffic, anomaly detection in encrypted traffic has
become a challenging problem. This study proposes an approach for anomaly detection in encrypted
HTTPS traffic using machine learning and compares the performance of different feature selection
techniques. The proposed approach uses a dataset of HTTPS traffic and applies various machine learning
models for anomaly detection. The study evaluates the performance of the models using various
evaluation metrics, including accuracy, precision, recall, F1-score, and area under the curve (AUC). The
results show that the proposed approach with feature selection outperforms other existing techniques for
anomaly detection in encrypted network traffic. However, the proposed approach has limitations, such
as the need for further optimization and the use of a single dataset for evaluation. The study provides
insights into the performance of different feature selection techniques and presents future research
directions for improving the proposed approach. Overall, the proposed approach can aid in the
development of more effective anomaly detection techniques in encrypted network traffic.

IoT-based healthcare solutions have transformed patient care and improved results. These technologies
capture and store sensitive patient data, raising security and privacy concerns. The paper first discusses
IoT-based healthcare system security issues. It evaluates cyber threats, system unavailability, and
vulnerabilities attackers can use to get patient data. The report also tackles IoT-based healthcare system
privacy issues such illegal data access and sharing and patient data breach concerns. The study proposes
numerous frameworks and solutions to protect patient data in IoT-based healthcare systems. Blockchain
technology can increase data security and privacy by offering a transparent, decentralized, tamperresistant database. The report also discusses differential privacy, a statistical method that adds noise to
sensitive patient data to protect privacy. The study also examines how AI and ML can detect and mitigate
security vulnerabilities in IoT-based healthcare systems. The study also emphasizes the need for uniform
security and privacy standards to protect patient data across healthcare companies. The report stresses
the importance of integrating security and privacy into IoT-based healthcare systems from the start. In
conclusion, the study emphasizes security and privacy in IoT-based healthcare systems and the problems
of protecting patient data. It explores standardized security and privacy protocols and many frameworks
and solutions to address these issues.

The radio spectrum is an underutilized natural resource with significant untapped potential. The rapid
proliferation of Internet of Things (IoT) devices is driving a dramatic increase in radio spectrum demand.
These devices can utilize cognitive radio (CR) technology to access the bandwidth left unused by
licensed spectrum users, also known as primary users (PUs), to meet end users spectrum requirements
efficiently. However, because PUs are given priority, CR-enabled Internet of Things (CR-IoT) devices,
also known as secondary users (SU), must frequently communicate with one another in an opportunistic
manner or under stringent power constraints. This can complicate CR-IoT device communication,
render it unstable, and restrict its throughput. This paper proposes a hybrid spectrum access algorithm
that combines underlay and interweave spectrum access methods to address this issue. When a PU is
detected, under the proposed scheme, CR-IoT devices utilize underlay spectrum access. In contrast,
when no PU is detected, CR-IoT devices employ interweave spectrum access. In addition, a proposed
iterative algorithm permits CR-IoT devices to adapt their sensing thresholds, and sensing time based on
the Signal-to-Noise ratio (SNR) received from Pus to strike a balance between achievable throughput
and fairness among PUs and CR-IoT devices in extremely noisy channel. Simulation results demonstrate
that the throughput for CR-IoT devices increases by 28% in proposed scheme when compared to the
conventional spectrum access schemes.