This paper presents a simulation-based framework to optimize 5G/6G mmWave network deployments in enterprise environments. Using ns-3 and NYUSIM, it evaluates next-generation Node B (gNB) placement, beamforming, and handover strategies across factory, office, and campus settings. Leveraging the inherent high bandwidth and low latency capabilities of mmWave technology, this study systematically addresses critical challenges such as severe signal attenuation, dynamic blockage, and efficient beam management in complex indoor and outdoor enterprise settings, including large-scale industrial complexes, multi-floor smart offices, and expansive university campuses. Utilizing established open-source network simulators, specifically ns-3, and integrating publicly available, industry-standard channel models such as 3GPP TR 38.901 and NYUSIM, the research proposes and rigorously evaluates novel deployment strategies, advanced beamforming techniques, and intelligent handover mechanisms. The anticipated outcomes include validated guidelines for optimal base station placement, robust performance benchmarks for key enterprise applications (e.g., Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC)), and a robust, extensible simulation framework. This work aims to provide critical, data-driven insights for telecommunication providers and network planners, enabling them to design and implement superior, reliable, and future-proof 5G/6G connectivity solutions, thereby accelerating digital transformation across various industrial and commercial sectors.
Smart cities represent a nexus where urban planning, engineering, digital technologies, and societal needs converge. In emerging economies such as Iraq, conventional top-down smart city models often fail to account for contextual realities, resulting in fragmented or unsustainable initiatives. This paper proposes a novel interdisciplinary smart city development framework that integrates Artificial Intelligence (AI)-based planning, engineering simulations, urban design heuristics, and insights from social sciences particularly those related to digital inclusion and governance. Leveraging publicly available datasets and simulation environments, we demonstrate that the proposed approach can reduce urban traffic congestion by up to 35%, improve equitable access to public services by over 30%, forecast energy demands with more than 85% accuracy, and detect cyber threats with a precision and recall of 85.7%. These results validate the feasibility of a modular, adaptable smart city blueprint that embeds cybersecurity and data governance principles from the outset offering a scalable alternative suited to the institutional and infrastructural realities of developing contexts like Iraq.
The growing sophistication of cyber threats exposes the limits of signature-based detection in Security Information and Event Management (SIEM) systems. User and Entity Behavior Analytics (UEBA) advances SIEM by enabling behavior-based anomaly detection, yet legacy approaches struggle with high false positives and poor adaptability to evolving threats. This research proposes an AI-driven UEBA framework that combines deep learning for modeling user behavior with graph-based tools to map system relationships, enhancing anomaly detection in enterprise environments. Using datasets such as CERT Insider Threat, UNSW-NB15, and TON_IoT, we simulate diverse behaviors and evaluate performance. Our Transformer-GNN ensemble achieved an F1-score of 0.90, reduced false positives by 40%, and cut incident triage time by 78% compared to rule-based SIEM. To support real-world use, we provide an open-source pipeline integrating with SIEM platforms via Kafka, Elastic search, and a modular ML inference layer. This work bridges AI research and deployable cybersecurity practice, advancing the development of adaptive, intelligent, and robust UEBA systems.