Mesopotamian Journal of CyberSecurity

The process security of money transactions is considered an important issue in e-banking. Additionally, it is an enormous problem if never controlled. particular, security should be a combination of fast and sturdy characteristics, which is the subject of previous studies suffered from. Our research attempts to improve the system in which banks deal with the security of financial transactions. This research leverages the idea of microsegmenting the entire system into designated zones to concentrate on security, where each zone has its own rules and limitations. These rules are managed by a smart contract, which decides whether they have been observed to verify the legitimacy of the customer. First, the two-phase commit algorithm (2PC) was used to specify the type of e-banking request. After this, the microsegmentation principle was applied to isolate each type of e-transaction process alone in a separate segment. Then, the yellow saddle goatfish algorithm (YSGA) was used to determine whether the smart contract conditions were optimized. Finally, if the customer is authorized, then the entire transaction process is saved in the blockchain's main ledger and secured by a unique hash. The blockchain application makes our system capable of dealing with large numbers of users in a decentralized manner. In addition, using a hash with each block prevents fraudulent transactions by adversaries. Our system has been examined against several recent well-known assaults/attacks, such as falsification, advanced persistent threat, bribery, spoofing, double spending, chosen text, race, and transaction replay attacks, and has proven to overcome them. In terms of the performance evaluation, we obtained an execution time of approximately 0.0056 nanoseconds, 3.75% complexity, and 1500 KB of memory and disk drive, which is considered low compared to that of state-of-the-art research. Thus, our proposed system is highly acceptable for banking sector applications.

Cutting-edge technologies have been widely employed in healthcare delivery, resulting in transformative advances and promising enhanced patient care, operational efficiency, and resource usage. However, the proliferation of networked devices and data-driven systems has created new cybersecurity threats that jeopardize the integrity, confidentiality, and availability of critical healthcare data. This review paper offers a comprehensive evaluation of the current state of cybersecurity in the context of smart healthcare, presenting a structured taxonomy of its existing cyber threats, mechanisms and essential roles. This study explored cybersecurity and smart healthcare systems (SHSs). It identified and discussed the most pressing cyber threats and attacks that SHSs face, including fake base stations, medjacking, and Sybil attacks. This study examined the security measures deployed to combat cyber threats and attacks in SHSs. These measures include cryptographic-based techniques, digital watermarking, digital steganography, and many others. Patient data protection, the prevention of data breaches, and the maintenance of SHS integrity and availability are some of the roles of cybersecurity in ensuring sustainable smart healthcare. The long-term viability of smart healthcare depends on the constant assessment of cyber risks that harm healthcare providers, patients, and professionals. This review aims to inform policymakers, healthcare practitioners, and technology stakeholders about the critical imperatives and best practices for fostering a secure and resilient smart healthcare ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, healthcare management, and sustainability research. Understanding the most recent cybersecurity measures is critical for controlling escalating cyber threats and attacks on SHSs and networks and encouraging intelligent healthcare delivery.

The awareness of cybersecurity among special needs students is necessary to help them stay safe while using technology. Recently, a good amount of interest has been drawn toward understanding the concepts and awareness of cybersecurity, and institutions have made efforts to help introduce awareness campaigns to help students understand the concepts of cybersafety, particularly for special needs students. The prior literature has focused primarily on exploring students' preferences, readiness, and experiences with cybersecurity. However, little attention has been given to measuring the level of cybersecurity awareness among students with special needs. To bridge this knowledge gap, the present study conducted an online survey to analyse the level of cybersecurity awareness and parental control among secondary school students with special needs aged 13 to 19 years in Malaysia. The study revealed that special needs students have a moderate level of cybersecurity awareness, with no significant difference among genders or academic streams. However, age does play a role in the level of awareness. Students with better cybersecurity knowledge are more satisfied with their online activities. Educating and monitoring special needs students on cyberattacks, password management, and phishing is crucial. In addition, parental control was found to be reasonable for most parents of students with special needs. This study contributes new knowledge by emphasizing the importance of parental control as a moderating variable in explanatory studies. It also highlights the need for further research in this area to expand the understanding of the importance of cybersecurity and how it can be implemented in specific school environments.

Authentication and confidentiality are critical elements in many systems, and passwords are used to protect these systems. One way to enter the password into the system is via voice. The problem with this type of password is that people listen to it easily. The general purpose of this study was to make the password based on lip movement instead of sound. To address this problem, a visual speech recognition (VSR) system was proposed that detects speech from lip movement as a silent password. The proposed system consists of preprocessing the captured video via the proposed algorithms and feature extraction and classification of spoken digits via a modified VGG16. Fairness, robustness, and privacy are also considered in the proposed system. Research has focused on Arabic digits because there is little research on Arabic visual speech recognition. The reported results of the model show remarkable performance, with a validation accuracy of 96.74%. It was concluded that the proposed system, which relies on analysing lip movement instead of sound, increases security by preventing others from hearing the spoken password.

Physically unclonable functions (PUFs) are advanced physical security measures that offer fundamental, unclonable appraisals of physical objects, providing an effective defense against hardware vulnerability breaches. They function as unique digital hardware fingerprints. This study discusses previous methods adopted for improving hardware security via PUF technology, with a specific focus on PUF circuits implemented on FPGA boards. Hardware security is assumed to be enhanced by adding a memristor to the ring oscillator PUF circuit and implementing these authentication architectures on FPGA boards. Additionally, this study explores methods for improving the main performance metrics for FPGA-based memristive-ring oscillator PUFs, including uniqueness, uniformity, and reliability. The study was founded on many scientific studies selected according to specific criteria. This study aims to assess and contrast these studies to achieve substantial enhancements in the security of devices on the basis of the obtained results. Upon comparing the findings, it was revealed that the proposed techniques, which provide flexibility and adaptability in dealing with memristive-PUF circuits to improve security services, displayed a distinct enhancement in security performance compared with other research that did not include any references to memristors. As an essential part of the authentication architecture, performance metrics involving memristor technology are verified in this study, with a uniqueness of 48.57%, uniformity of 51.43%, and bit-aliasing of 51.43%. These outcomes demonstrate the validation of memristor-based physical unclonable functions (M-PUF) against encryption and verification within a certified key exchange and tests.

This paper presents the quantum-inspired substitution box (QIS-Box) algorithm, a novel approach aimed at enhancing the security of cryptographic S-boxes by leveraging quantum-inspired techniques. The QIS-Box algorithm significantly improves key cryptographic metrics, increasing nonlinearity from 102--110 and reducing differential uniformity from 6--4. Nonlinearity, which measures an S-Box's resistance to linear cryptanalysis, is enhanced to provide stronger protection, whereas differential uniformity, which assesses resilience to differential cryptanalysis, is improved to offer greater defence. These advancements highlight the robust security capabilities of the QIS-Box algorithm against prevalent cryptanalytic attacks. Furthermore, the algorithm demonstrates notable efficiency, making it well suited for implementation in resource-constrained environments such as IoT devices. This research contributes substantially to the development of quantum-resistant cryptographic solutions, addressing the challenges posed by emerging quantum computing technologies. Future research will focus on refining the simulation of quantum events within classical computational frameworks and integrating the QIS-Box Algorithm with other cryptographic techniques to further increase security and efficiency. This study paves the way for the development of advanced cryptographic systems capable of withstanding the evolving landscape of digital threats.

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting abnormal network behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, such as denial of service, spam, phishing, and other types of attacks, are reported daily, and the number of attacks is growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposes an anomaly intrusion detection system method based on a new RNA encoding method and the ResNet50 Model, where encoding is performed by splitting the training records into different groups. These groups are protocol, service, flag, and digit, and each group is represented by the number of RNA characters that can represent the group's values. The RNA encoding phase converts network traffic records into RNA sequences, allowing for a comprehensive representation of the dataset. The detection model, which uses the ResNet architecture, effectively addresses training challenges and achieves high detection rates for different attack types. The KDD-Cup99 dataset is used for both training and testing. The testing dataset includes new attacks that do not appear in the training dataset, which means that the system can detect new attacks in the future. The efficiency of the suggested anomaly intrusion detection system is determined by calculating the detection rate (DR), false alarm rate (FAR), and accuracy. The achieved DR, FAR, and accuracy are 96.24%, 6.133%, and 95.99%, respectively. The experimental results revealed that the RNA encoding method can improve intrusion detection.