An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.

In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents "Scrum Spiral" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of "ScrumSpiral" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named "Scrum Spiral" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model "Scrum Spiral" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.