Dyala Ibrahim

A hybrid parallel storage system is a system which has a hierarchy of storage; each is a primary or secondary storage device, which has significantly different rates of reading the data. The more one goes up the storage hierarchy, greater will be the speed of data access. Moving critical application data to higher levels can greatly lessen application I/O wait time. In general, it assists in cutting down the total time consumption of the app to finish a process. This study presents a prototype of a two-level parallel hybrid storage system with SSD and HDD. The proposed system uses data mining methods to systematically evaluate and classify the application data. While the application runs and requests data, the solution at the same time works to reactively predict what data will be useful next. Data blocks that are classified as high priority are automatically elevated to SSD within the same hybrid cloud storage disk group. With the help of simulation, the data migration strategy backed up by data mining will improve user productivity. The application execution time decreases significantly if any of the application’s data access trace is particular and over 57.21% is used. New hybrid storage architectures paired with predictive data management help applications run faster than ever before.

Code reviews have always been a necessary evil in software development but one that was very time consuming. Nevertheless, it is much tougher for educators in education due to evaluating codes for hundreds of students. This paper describes the design and deployment of an AI-assisted code review system in a university computer science department. The system uses the transformer and GPT-4 models for aiding the human graders in assessing programming assignments of various university courses. The system checks the code for syntax and semantic errors, design and implementation patterns, and compliance with coding standards. In a six-month study run of involving 150 student projects, the automated system detected around 87% of code quality issues needing human inspection. It cut down the time taken by educators to review code by approximately 62%, with the same grade of feedback quality. Based on our experience, AI for code review has exciting potentials but also currently has limitations in an academic environment. It is good at spotting common mistakes as well as style issues, but not so good at picking up on context-sensitive design decisions or pedagogical issues. The study provides evidence of AI-supported code review for the academic field and suggestions for educators wishing to set up a similar system.

In recent years, a range of innovative approaches have been developed to tackle decision-making challenges more effectively. These approaches include concepts such as probability theory, fuzzy set theory, intuitionistic fuzzy sets, vague sets, interval mathematics, and rough set theory. These tools each offer unique approaches to managing uncertainty and imprecision, which are common when working with complex systems. One major issue in software development is failure to meet security requirements. This can be caused by a variety of factors, such as unclear security specifications, evolving threat landscapes, or gaps in communication among stakeholders. To address this persistent problem, this paper introduces a new method designed to quantify security requirements more accurately and streamline the decision-making process for assessing security risks. This approach is based on fuzzy soft set theory, which allows for more flexible and precise handling of uncertain or ambiguous information. By using this method, software engineers and security professionals can gain a clearer understanding of security risks and make better-informed decisions when building secure systems. To demonstrate the practical value of the proposed method, a case study is conducted to evaluate its effectiveness. The results of this case study suggest that security administrators can use this method to implement more targeted and efficient security measures, ensuring that the systems they develop are more resilient to potential threats. Received: 20 July 2025 | Revised: 20 October 2025 | Accepted: 31 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Dyala Ibrahim: Conceptualization, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Conceptualization, Methodology, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Lial Raja Alzabin: Software, Resources, Data curation, Visualization. Ahmad Alajarmeh: Validation, Visualization. Ahmad Reda Alzighaibi: Validation, Formal analysis, Writing – original draft. Hasan Hashim: Investigation. El-Sayed Atlam: Investigation. Malik Jawarneh: Investigation.

In today's rapidly evolving digital landscape, the importance of data security cannot be overstated. Every day, vast amounts of information are generated and shared, with each data type (whether text, image, audio, or video) possessing its own unique characteristics and vulnerabilities. Among these, image data stand out due to their intricate structure and the sheer volume of sensitive information that they can contain. Images are different from text data. Text data are straightforward data and are easy to protect. Images are difficult to protect because they are not straightforward. Ways used for encryption have long been considered suitable for encrypting image data. However, currently, they are not. Criminals are developing techniques every day to defeat the right-spotting methods of the business world. Further, this prompts scientists and professionals to find effective solutions. This is where artificial intelligence (AI) comes into play. Techniques for securing images enhanced by AI have the potential to improve image encryption significantly in a manner that increases both protective security efficiency and processing efficiency. This paper discusses various image encryption techniques in detail, including both ancient and modern methods for protecting visual information. It also discusses AI that helps in enhancing the accuracy and efficacy of these encryption techniques. When it comes to encryption of images, AI makes it more powerful. It can also counter newer threats that are emerging. Therefore, security is more dependable and efficient. This paper gives a detailed review of state-of-the-art methods and their effectiveness over traditional methods. By delving into various AI techniques for image encryption, this paper shows that the incorporation of these techniques makes image encryption more secure and efficient. Received: 25 July 2025 | Revised: 18 September 2025 | Accepted: 11 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Dyala Ibrahim: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Methodology, Software, Validation, Investigation, Writing – original draft, Supervision, Project administration. Ahmad Reda Alzighaibi: Formal analysis, Writing – original draft, Visualization. Hasan Hashim: Writing – original draft, Visualization. El-Sayed Atlam: Writing – original draft, Visualization.

The complexity of existing software is shown in the requirement for specific test protocols to ensure robustness, functionality, and performance. If traditional software testing methods are unable to cover the existing defects and weak parts of such a large software pool, then it is a limit of the traditional methods and thus, new methods are required to enable detecting such defects and weaknesses inside the software pool. This article suggests a new framework within the area of software testing based on artificial intel­ligence (AI). This is also included since one of the goals of the framework is to facilitate the development of tools that can be used to detect bugs as well as increase the robustness of the code. Extending the above architecture to natural language processes, machine learning and machine learning models of aberrant behaviour allows the intelligent solution of the problem of automated testing. In the study, a comparison is made between the proposed solution and existing testing practices citing benefits in terms of efficiency, accuracy, and the proportion of defects that are addressed. The frameworks work in practical application which is evidenced by the outcome of the case studies and controlled tests filling in a solution that is effective for the software problems that are rampant in contemporary society. DOI:https://doi.org/10.17762/ijisae.v12i23S.7147

Hundreds of millions of people worldwide use computing devices and services, including smartphones, laptops, and messaging apps. Visual cryptography (VC) is one of the most secure encryption methods for image encryption in many applications, such as voting security, online transaction security, and privacy protection. An essential step in VC is encrypting secret images into multiple digital shares to hide them with the intention of successfully reverting them to their original form. Hence, a single share cannot reveal information about the secret image. Issues including pixel enlargement, high processing costs, and low decryption quality influence the current state of VC. We address these issues by introducing a novel technique based on (2, 2) secret sharing and the algorithm of Harris hawks optimization (HHO) for color photos. For the encryption process, the appropriate color levels are determined using the HHO algorithm. Consequently, images are decrypted with improved quality and a small impact on the overall processing complexity. The suggested scheme is also non-expandable due to the equal size of the initial secret image and the shared images. This results in lower memory requirements and improved image quality. The approach is applied to a set of well-known benchmark images. Moreover, a set of standard metrics is used to assess the robustness of the proposed scheme, including its capability in defending against cryptanalytic attacks, a correlation, a histogram, and the quality of encryption. According to the findings, the proposed solution provides better reconstructed image quality, time-efficient encryption, and nearly optimal statistical properties compared to previous approaches.

Software-defined networking (SDN) is a unique network architecture isolating the network control plane from the data plane, offering programmable elastic features that allow network operators to monitor their networks and efficiently manage them. However, the new technology is security deficient. A DDoS attack is one of the common attacks that threaten SDN controllers, leading to the degradation or even collapse of the entire SDN network. Entropy-based approaches and their variants are considered the most efficient approaches to detecting DDoS attacks on SDN controllers. Therefore, this work analyzes the feasibility and impacts of an entropy-based DDoS attack detection approach for detecting low-rate and high-rate DDoS attacks against the controller, measured in terms of detection rate (DR) and false-positive rate (FPR), triggered by a single or multiple host attacks targeting a single or multiple victims. Eight simulation scenarios, representing low and high DDoS attack traffic rates on the controller, have been used to evaluate an entropy-based DDoS attack detection approach. The experimental results reveal that the entropy-based approach enhances the average DR for detecting high-rate DDoS attack traffic compared with low-rate DDoS attack traffic by 6.25%, 20.26%, 6.74%, and 8.81%. In addition, it reduces the average FPRs for detecting a high DDoS attack traffic rate compared with a low DDoS attack traffic rate by 67.68%, 77.54%, 66.94%, and 64.81.

This paper introduces a new “Facial Recognition (FR)” approach based on a new relatively optimization method. This new FR approach is used to mitigate the limit

The Internet of Things (IoT) plays a well-known role in connecting physical and virtual objects to exchange information. IoT environments can connect billions o