The rise of cyberattacks has led to an increase in the creation of fake websites by attackers, who use these sites for advertising products, transmit malware, or steal valuable login credentials. Phishing, the act of soliciting sensitive information from users by masquerading as a trustworthy entity, is a common technique used by attackers to achieve their goals. Spoofed websites and email spoofing are often used in phishing attacks, with spoofed emails redirecting users to phishing websites in order to trick them into revealing their personal information. Traditional solutions for detecting phishing websites rely on signature-based approaches that are not effective in detecting newly created spoofed websites. To address this challenge, researchers have been exploring machine-learning methods for detecting phishing websites. In this paper, we suggest a new approach that combines the use of blacklists and machine learning techniques such that a variety of powerful features, including domain-based features, abnormal features, and abnormal features based on URLs, HTML, and JavaScript, to rank web pages and improve classification accuracy. Our experimental results show that using the proposed approach, the random forest classifier offers the best accuracy of 93%, with FPR and FNR as 0.12 and 0.02, with a Precision of 90%, Recall of 97% an F1 Score of 93%, and MCC of 0.85.

The phrase “data visualization” has taken on a lot of significance in today's world since it allows users of any level of expertise to comprehend data. Data visualization is a technique that turns a set of small and large raw data into visual data so that the user is capable of analyzing, comprehending, and discovering correlations, patterns, and trends from the data. This research study reviews some types of data visualization and their primary uses. In addition, there is a great need for data visualization in many fields, and this research focuses on the field of sales, where it uses two sales-related data sets to show the sales analysis and generates an interactive dashboard with a variety of data visualizations by using the Power BI tool, which is a service for business analytics and is extensively utilized for business intelligence, reporting, and data analysis across numerous industries.

Breast cancer continues to be one of the most prevalent and lethal cancers impacting women globally. Given the significant rates of occurrence and death linked to late diagnosis, it is essential to create precise and timely detection techniques. This research explores the use of machine learning algorithms to categorize tumor samples from the Breast Cancer Wisconsin (Original) dataset into benign or malignant types. A thorough preprocessing pipeline was developed, incorporating missing value treatment, feature scaling, and class balancing to improve data quality and model efficacy. Ten classifiers were assessed, which included Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, and Neural Networks. The findings highlighted marked enhancements in precision following optimization, emphasizing the efficacy of customized preprocessing methods. The research emphasizes the promise of machine learning for aiding in early breast cancer detection and suggests avenues for future studies centered on larger datasets, enhanced feature engineering, and interpretability.

Breast cancer is a prevalent disease affecting millions of women around the world. A key factor in improving the outcome of patients with breast cancer is early detection and classification. The use of convolutional neural networks (CNNs) has shown promising results for the analysis of various medical images, including the classification of breast cancer. This paper presents an overview of the breast cancer classification problem and demonstrates how a CNN can be effectively utilized for this task. Additionally, numerous papers have been presented and compared in terms of CNN structures, datasets, images, and accuracy. Different CNN models have been found to be effective at detecting breast cancer, which affects its accuracy. It should be recognized, however, that the accuracy of this algorithm depends on both the size of the dataset and the number of images that are used. As a result, it can be concluded that the number of images, datasets, or even the CNN approach can be used case-by-case to have higher accuracy. Finally, the results of accuracy should be expanded based on the analysis of one parameter in upcoming research. As soon as the best accuracy has been achieved, additional parameters may be added.

Purpose This study examines the impact of institutional ownership (IO) on firm performance. It also investigates whether powerful CEOs using a “CEO score index” moderate IO and firm performance nexus by drawing on insights from the agency and resource dependency theories. Design/methodology/approach Data were obtained from annual reports of companies listed on the Palestine Security Exchange from 2009 to 2019. Panel data regressions were conducted based on 528 observations. In addition, this study repeated the analysis using a one-step generalized method of moments (GMM) and two-stage least squares analysis to deal with the endogeneity issue. Findings Results show that IO and CEO power is positively associated with firm performance. Besides, it has been established that CEO power strengthens the relationship between IO and performance. Thus, this can be summarized that IO improves firm performance; however, with the powerful CEO intervention, the performance will improve even more. Originality/value Studying IO is timely given since the type of ownership is paramount to identify which form of a high degree of ownership affects the performance negatively, especially, in the Palestine environment which is dominated by institutional investors. This is of great importance to the investors as it will enable them to identify the type of firms to which they can commit their funds, and which firm excels through the CEO power. Besides, the inconsistency results in previous literature on IO, and firm performance indicates that there is an indirect effect that needs alternative explanations.

This study evaluated the effectiveness of a malware awareness program designed to enhance college students’ knowledge of malware prevention strategies and promote responsible online behavior. The program group discussions and presentations are designed to address misconceptions and improve students’ understanding of malware prevention, including identifying security attacks, understanding malware spreading mechanisms, and avoiding risky online behavior. A study was conducted following the pre-test and post-test assessments to measure the program's impact on students’ knowledge levels. The results revealed significant improvements in post-test scores indicating the program's success in achieving its objectives. The observed enhancements in students’ knowledge can be attributed to the program's focus on delivery techniques, which have been proven effective in enhancing knowledge retention and promoting learning. The implications of this study underscore the value of providing targeted and engaging cybersecurity education to college students. By incorporating malware awareness programs into the educational system, institutions can foster a more secure digital environment and promote a culture of cyber-resilience among future professionals. Furthermore, the program's success suggests that similar interventions may be beneficial in other areas of cybersecurity, encouraging ongoing research and development of educational initiatives that address the diverse range of threats in today's interconnected world.

This study aims to understand the relationship between Facebook users’ personalities and their usage pat-terns, with a focus on extraversion traits. Despite the growing body of research on this topic, there remains a scarcity of comprehensive literature reviews This research attempts to synthesize literature through a review where 45 studies linked extraversion personality traits to Facebook. Researchers discussed Extra-version and Facebook using various studies between 2015 and 2021 identified with the ELSIEVER engine. The findings of this study spotlight extraversion personality in a marketing and business context, offer-ing insights into potential applications and implications. A conceptual model is proposed to elucidate the role of extraversion in various functional areas of business, including marketing, sales enhancement, pro-motion, and customer engagement. Moreover, this study offers theoretical and practical implications for practitioners and researchers alike, while also providing suggestions for future research and noticed gaps.

In the contemporary digital realm, the widespread dissemination of false Arabic news is a significant social concern laden with various risks. Recognizing the seriousness of this issue, our research utilizes cutting-edge technologies, specifically Machine Learning, to distinguish between authentic Arabic news and deceptive counterparts. The consequences of propagating misinformation go beyond compromising social cohesion; they extend to the erosion of digital information's credibility, fostering an atmosphere of mistrust and deception that undermines the very foundations of societal bonds. Through the application of contemporary technologies, this study aims to identify and underscore Arabic news that embodies such risks. The intricate characteristics of Arabic language morphology, marked by words carrying multiple meanings based on inflectional forms and the prevalence of numerous diacritical marks, intensify the challenges of text classification. Despite contending with these linguistic intricacies, modern natural language processing approaches offer practical solutions. Notably, our methodology relies on the preprocessing of two available datasets for training and testing, a crucial step for seamlessly integrating a range of Machine Learning techniques, including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and Multinomial Naive Bayes (NB). Importantly, the Logistic Regression technique emerged as the most effective, achieving an accuracy of 95.92% in the SANAD dataset for discerning nuanced Arabic news

Agriculture has a huge benefit on the global landscape due to rapid population growth and the subsequent increase in demand for food. Hence, there is an urgent need to enhance crop productivity. One of the primary factors contributing to low crop productivity is the spread of diseases caused by bacteria, spores, fungi, and viruses. However, this problem can be mitigated by applying plant disease detection methods, Recently, the field of machine learning and deep learning has achieved remarkable results when combined with IoT -based technology, especially in the field of plant disease recognition. This scientific article aims to clarify the prevailing methodologies used in IoT -based smart agriculture, with a clear focus on the use of IoT and machine learning. In this context, the manuscript presents an innovative model for plant disease detection and recognition, drawing on the fields of machine learning and deep learning, ultimately leading to increased accuracy and efficiency. In essence, this article provides a comprehensive review and overview of various machine learning and deep learning techniques used in plant disease detection, using an IoT approach.

Social engineering is hacking and manipulating people's minds to obtain access to networks and systems in order to acquire sensitive data. A social engineering attack happens when victims are unaware of the strategies utilised and how to avoid them. Although rapid developments in communication technology made communication between individuals easier and faster, on the other hand, individuals' personal and private information is likely to be available online via social networking or other services without adequate security measures to protect such sensitive data. Hackers can use social engineering to target them no matter the technology they use to protect themselves. The methods differ, and the goal is the same, which is to manipulate and deceive organisations, companies, and individuals to obtain sensitive and private in-formation that attackers can benefit from, perhaps to sell it on the dark web or steal the payment card information of victims. The current research presents the attack techniques used in social engineering, as well as ways for pre-venting social engineering assaults. The major purpose of this study is to systematically and impartially conduct a systematic review of previous research on current social engineering attacks and the methods used to reduce these attacks.

This paper proposed an approach to enhance students performance and smoothen their integration in the university academic life. A social media forum is also proposed to ongoing communication to ease the life of the students and provide them with an exceptional opportunity to seek assistance and suppoirt from their peers and teachers. The research results exhibit that the majority of the students support and are eager to see this idea live and willing to play an active role and show full commitment. The cosortuion as a platform prepares the students, fosters and enables them to a smoothen transition to university, as well as improving their communication skills and academic performance by using mentoring, tutoring, and coaching techniques. Facebook was used as a communication and interactive tool among group members. The theme behind this platform is to construct academic group from final year school students, first year university students, school teachers, and university teachers. Each group has a mentor, coach, and tutor. Each member will play a specific role throughout the group, which will be highlighted in this paper. The outcomes were promising and interesting for both students, and their parents, also the teachers involved. It is recommended to dissiminate this experience and publicise it.

The growth of data exchange and the dependency on the digital world through cyberspace raise security risks.Social engineering attacks occupy high percentage of total cybercrimes.It is also classified as the major cause of financial losses in cyberspace.This shows the need to clarify social engineering definition and clarify the proposed frameworks solutions by different researchers.This paper explores the previous researches that try to extract different concepts and perspectives which are called lifecycles, phases, frameworks, models, or a mix of them, knowing this and the development of the framework help us facing the threat of social engineering.Most of the studies agree on the effect of a comprehensive framework and how it affects positively.The results express the need for more empirical studies, government permissions, financial support to improve the conceptual frameworks to apply them to a wide range of societies, and more focus on awareness responsibility for government, users, and organizations.This study is A systematic literature review that uses Prisma methodology to extract and analyze eligible research criteria to achieve the objective of describing social engineering attack frameworks comprehensively This paper finds are the urgent need for an empirical comprehensive conceptual framework and government support to facilitate access to data, education curriculum, and enact laws.On the other hand, it emphasizes limitations, risk of bias, and future work in each study.In future work, the author suggests adding the defense perspective as a phase.

Wearable devices are becoming increasingly popular, with users adopting them for a wide range of purposes. For example, fitness equipment can now perform new functions such as shopping or purchasing train tickets using contactless payments. Furthermore, fitness trackers gather a variety of personal data, including body temperature, pulse rate, eating habits, body weight, steps, distance travelled, calories burned, and sleep stage. Although these devices can be helpful to customers, more and more publications are warning about the cybersecurity threats they pose and the potential for them to be hijacked and used as launching grounds for other systems. Furthermore, because of their wireless broadcasts, these devices might be vulnerable to a malicious attack, exposing the data they gather. In addition, these devices are vulnerable due to a lack of authentication, Bluetooth connection difficulties, location monitoring, and third-party vulnerabilities. The purpose of this article is to offer consumers cybersecurity recommendations so that they may take precautions when using fitness devices.

This paper focuses on the challenges and issues of detecting malware in to-day's world where cyberattacks continue to grow in number and complexity. The paper reviews current trends and technologies in malware detection and the limitations of existing detection methods such as signature-based detection and heuristic analysis. The emergence of new types of malware, such as file-less malware, is also discussed, along with the need for real-time detection and response. The research methodology used in this paper is presented, which includes a literature review of recent papers on the topic, keyword searches, and analysis and representation methods used in each study. In this paper, the authors aim to address the key issues and challenges in detecting malware today, the current trends and technologies in malware detection, and the limitations of existing methods. They also explore emerging threats and trends in malware attacks and highlight future directions for research and development in the field. To achieve this, the authors use a research methodology that involves a literature review of recent papers related to the topic. They focus on detecting and analyzing methods, as well as representation and extraction methods used in each study. Finally, they classify the literature re-view, and through reading and criticism, highlight future trends and problems in the field of malware detection.

This empirical study aims to examine the customer awareness and experience in both commercial and Islamic banks in the UK. It pays attention to the quality of services and the available support given to the customers. Banks pride in their financial services and support for customers. The banks’ mantra is about treating customers as priced assets and providing them with all the necessary support and guidance. Undoubtedly, the customers’ awareness of what the banks offer to them in terms of money safety, transfers, loans and interest rates are very important. The study utilises a questionnaire and focus group of 18 respondents with customers who use both banks to gain an understanding of their experiences. Evidence suggests that there is a general understanding of the banks’ services and commercial banks in particular expose their services more widely using various communication channels. The study found that some customers are aware of the specific charges and the interest rates. However, some were unaware of the services offered by the Islamic banks in relation to mortgage and interest rates and this had impact on customers’ satisfaction and loyalty. The study concludes that both commercial and Islamic banks need to do more to increase their customers services provisions in order to attract and retain existing customers. Offering customers and making them aware of a range of products and services tailored to their needs is way forward to acquire new and to increase customer retention.

Technology has changed the way people live .The role of school leadership, teaching approaches, and school innovation have also changed in the industrial era 4.0 due to advanced technology such as Artificial Intelligence and the internet.Moreover, the challenges facing school administrators today different than their predecessors, since many factors influence the integration of technology in schools some of these are lack of ICT training, teachers 'competence in ICT, and access to ICT resources.Considering Leadership is the key agent in the effective implementation of technology in schools.This research aimed to investigate the influence of principals' technology leadership and professional development on teacher's technology integration with gender and experience as moderation variables.In this cross-sectional survey, random sampling was carried out to select 442 principals and 953 teachers from Palestinian public schools.Two different questionnaires were used the first one was based on National Education Technology Standards -Administrator, NETS-A (2014) and Survey of Technology Experiences for school principals while the second instrument is Learning with ICT: Measuring ICT Use in the Curriculum for the teachers.Numerical data were analyzed quantitatively using two software the Statistical Package for the Social Sciences SPSS Version 23.0 and Smart PLS.The finding showed that the levels of Technology Leadership of the five constructs (systemic improvement, visionary leadership, excellence in professional practice, digital age learning culture, and digital citizenship), professional development and teacher's technology integration were at high levels.Based on the results of the data analyses there is a positive significant relationship between the five constructs of technology leadership and professional development with teacher's technology integration in the Palestinian public schools in the west bank.

The term internet of things (IoT) has gained much popularity in the last decade, which can be defined as various connected devices over the internet. IoT has rapidly spread to include all aspects of our lives. For instance, smart houses, smart cities, and variant wearable devices. IoT devices work to do their desired goals, which is to develop a person's living with his/her minimal involvement. At the same time, IoT devices have many weaknesses, which attackers exploit to affect these devices' security. Denial of Service (DoS) and Distributed Denial of Service (DDoS) are considered the most common attacks that strike IoT security. The main aim of these attacks is to make victim systems down and inaccessible for legitimate users by malicious malware. This paper's objective is to discuss and review security issues related to DoS/DDoS attacks and their countermeasures i.e. prevention based on IoT devices' layers structure.