Faisal Aburub’s research while affiliated with Petra University and other places

This chapter presents a comprehensive analysis of advanced social engineering attack vectors enhanced by Generative Artificial Intelligence (GenAI) technologies, focusing on impersonation methodologies and synthetic content generation. The research employs a systematic evaluation framework to analyze attack patterns leveraging Large Language Models (LLMs) and deepfake architectures within social engineering campaigns. Through quantitative analysis, we investigate the efficacy of GenAI-driven phishing content, examining success rates, behavioral patterns, and attack sophistication metrics across multiple enterprise environments. The study presents empirical evidence demonstrating a 45% increase in attack effectiveness when leveraging AI-driven impersonation techniques compared to traditional methods. Our primary contribution encompasses the development and validation of novel detection methodologies and defensive frameworks engineered for AI-enhanced social engineering threats.

Developing countries face heightened cybersecurity risks due to the accelerating adoption of generative AI tools. These technologies have created new vulnerabilities and widened existing protection gaps, especially in resource-constrained regions. This chapter investigates how the rapid expansion of generative AI intensifies threats in emerging economies by examining malicious applications, unauthorized data generation, and social engineering exploits. It aims to uncover approaches that bolster defenses through human-centric methods and responsible governance mechanisms. Analysis of empirical studies indicates that generative AI already affects many sectors, leaving individuals and institutions exposed to novel attack vectors. Findings suggest targeted policy actions and capacity-building measures can mitigate these risks. The chapter concludes by underscoring human oversight and ethical AI deployment as essential countermeasures. This work contributes strategic guidance for safeguarding digitally evolving societies.

Organizations continue facing threats that exploit network vulnerabilities. Defenders seek approaches that detect anomalies and respond quickly to attacks. This chapter examines predictive models and automated mechanisms for network defense. It explores how artificial intelligence approaches learn evolving patterns in large-scale data, offering insights and mitigation strategies. Researchers observe advantages in automated recognition, anomaly forecasts, and coordinated countermeasures. Studies indicate success in reducing detection delays and halting malicious actions. Defense frameworks featuring reinforcement agents, neural architectures, and adaptive analytics show promise in lessening manual involvement. Outcomes suggest that ongoing refinements in data handling, architecture design, and interpretability are essential. This chapter provides a synthesis of methods, findings, and future research directions for next-generation AI-driven network defense.

Urban settings in developing contexts face pressures arising from population growth, restricted resources, and evolving policy demands. Research insights from universities often fail to guide governance actions or inform new strategies. This chapter examines how academic leadership can connect theoretical work with practical applications in contexts that adopt emerging digital tools. The purpose is to assess ways to align faculty resources, research units, municipal agencies, and community stakeholders. Findings reveal that administrative support, collaborative programs, and targeted capacity-building foster tangible outcomes in key areas such as infrastructure, resource management, and service delivery. The chapter concludes that universities can accelerate digital adoption by shaping policy agendas and funding pathways, providing a structured route toward more effective governance outcomes.

Organizations operate in a global digital landscape where cyber threats continue to escalate in both complexity and volume, rendering traditional security models insufficient. This chapter addresses the gap in understanding how data-driven methods, enriched by b usiness intelligence, can enhance protective measures for digital assets and infrastructure. It aims to explore the interplay between predictive analytics, automated detection, and proactive incident response to strengthen overall security postures. The methodology involves synthesizing leading industry and governmental reports with academic literature to highlight critical insights. Findings demonstrate that embedding business intelligence within cybersecurity ecosystems yields improved threat visibility, reduced response times, and a more resilient security architecture.

Business intelligence (BI) systems are integral to strategic and operational decision-making in contemporary data-driven organizations. As these platforms accumulate increasingly large and intricate datasets, they become prime targets for sophisticated cyber threats. This chapter examines how artificial intelligence (AI) and machine learning (ML) methods fortify the security of BI environments and mitigate the escalating incidence of advanced attacks. The discussion reviews BI's evolution from static reporting functionalities to dynamic, real-time analytics, a transformation that parallels increased vulnerabilities and novel adversarial tactics. It then analyzes the theoretical foundations of AI- and ML-based threat detection, encompassing anomaly detection, adversarial machine learning defenses, and explainable AI (XAI).

Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clustering method allows a data point to belong to multiple clusters with a certain degree of membership, making them a more flexible technique compared to well-known hard clustering methods. Subsequently, the article discusses the problems and limitations of fuzzy clustering in economics, computational complexity, fuzzy parameters and uncertainty, and result interpretation.Contributions include fuzzy clustering to other machine learning methods, apply fuzzy clustering to big data, and fuzzy clustering to improve economy policy making. In conclusion fuzzy clustering represents a precious resource for in-depth and timely insights which can contribute to policy maker, business and researcher developments by discovering new target areas to invest, consequently increasing fountains of knowledge.

This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like structure. Fundamental graph-theoretic concepts such as homomorphisms, isomorphisms, and structural invariants (including degree sequences and connectivity measures) are rigorously redefined within this fuzzy context, with detailed proofs and illustrative examples provided. Through step-by-step computations and visualizations using this concept, we highlight how our approach not only recovers classical crisp graph properties as a special case but also offers enhanced analytical capabilities for modeling real-world networks characterized by uncertainty. Additionally, potential extensions to intuitionistic fuzzy graphs, interval-valued fuzzy graphs, and multi-attribute fuzzy structures are discussed, along with computational implications and applications in network analysis and decision support systems. This framework's consistency and completeness were validated through rigorous proofs, ensuring that all fuzzy operations remain coherent with their classical counterparts. Moreover, the framework facilitates efficient algorithm design and opens new research directions, thereby providing a unified platform for both theoretical advancements and practical applications in complex network analysis.

Today, in the rapidly changing digital world, combining AI with traditional marketing can yield tremendous results. This paper elucidates the potential of AI for automation and customization in digital marketing. There are two chief issues with old-school approaches to online advertising: they cannot scale well enough and thus cannot be customized for use on different demographics. In addition, targeting inaccuracies due to manual processes may lead to ineffective campaigns. To overcome these hurdles, this research suggests the integration of Automation and Personalization using Artificial Intelligence Technology (AP-AIT) into digital marketing strategies. The utility of AP-AIT permits marketers to cope with massive volumes of campaigns at the same time as providing personalised content material and reviews for each customer phase. The real-time statistics evaluation also enabled with the aid of machine learning capabilities helps AP-AIT optimize engagement metrics ensuing in stepped forward performance on conversion rates which finally ends in improved client loyalty over an prolonged period of time. The outcomes acquired from this observe provide a strong basis for knowledge the profound implications that AP-AIT has in remodeling how corporations interact their clients digitally via computerized personalisation strategies driven by way of synthetic intelligence programs along with chat bots. Businesses who contain AI’s automation—pushed personalization can acquire unmatched performance degrees, effectiveness costs in addition to general satisfaction among customer undertaking such advertising initiatives as by no means seen before.

This study aims to understand the impact of business intelligence (BI) capabilities on the competitive performance of supermarkets in Jordan. Data were collected through surveys conducted on 239 managers within supermarkets in Jordan. SEM analysis was used to explore the direct effects of business intelligence capabilities on competitive performance. The results showed a positive impact of business intelligence capabilities on the competitive performance of supermarkets in Jordan. Recommendations included investing in business intelligence infrastructure and talent development, fostering a culture of data-driven decision-making, and leveraging analytics to gain insights into customer behavior, market trends, and competitor strategies. This study provides practical insights for supermarket managers and executives seeking to improve the performance of their stores and achieve a competitive advantage in the Jordanian supermarket market.