رياض غافل عبد الحسين الخفاجي

Abstract. Software-defined networking (SDN) infrastructures are facing a growing menace from cyber threats, which leave them vulnerable to distributed denial of service (DDoS) attacks. This study extends the earlier research [Detecting DDOS attacks in SDN Networks Using Machine Learning Techniques], which extensively investigated vulnerabilities in the SDN architecture for DDoS attacks. We develop and evaluate machine learning methods that are specifically tailored to protect software-defined networks (SDN) from such malicious attacks. The centralized and rigorous operational protocol of SDN has enabled us to develop a range of detecting methods. This study examines the efficacy of different machine learning algorithms, including XGBoost, Native Bayes, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). The algorithm's computational efficiency and precision were evaluated using a dataset specifically created to simulate the intricate and unpredictable characteristics of network traffic. The experimental findings demonstrate that the XGBoost and Random Forest algorithms exhibit commendable performance in terms of both accuracy and speed. The precision varies between 99.26% and 77.0%, depending on the specific detection algorithm employed and the selected features. Consequently, these algorithms are well-suited for promptly dealing with and reducing potential hazards. XGBoost demonstrated exceptional versatility by maintaining accuracy across several testing scenarios while also reaching great processing efficiency. Utilizing machine learning has the potential to significantly enhance the security of SDN systems, as indicated by the results. This revelation has two significant ramifications: This study enhances our comprehension of efficient SDN DDoS mitigation strategies and sets a standard for the next research on integrating machine learning in security frameworks. We offer essential knowledge that aids in preserving vital network infrastructures in an ever more unpredictable digital landscape.

Several well-known Unmanned Aerial Vehicle (UAV) mobility models that make use of cellular networks will be compared in this study. The acquisition of services for ground-based User Equipment (UE) from Drone Base Stations (DBSs) is the primary focus of the examination. The four distinguishable mobility models— Random Waypoint (RWP), Straight Line (SL), Random Stop (RS), and Random Walk (RW)—are analysed and compared in this work. The UDM and UIM are two service models that are researched in this study. The primary contribution of this work is the development of a thorough method for investigating the point process of DBSs across different mobility and service models. The study compares the basic SL mobility model to more complex models that incorporate curved trajectories and finds performance disparities between the two. It also looks at the average session and received pricing of standard user equipment (UEs). The results of this study shed light on how well drone mobility models perform in cellular network settings, which can help with the development and refinement of drone-optimized cellular networks.

Remote sensing, specifically satellite imagery, and is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services.