Security assessment and ddos attack detection for internet of things (IoT) devices

The number of Internet of Things (IoT) devices is accelerated, making them an integral aspect of daily life. IoT indicates a network architecture that enables physical objects or devices to communicate with each other and cloud systems via the internet. These objects are equipped with sensors, software, and other technologies, allowing collect, analyze, and exchange of data. IoT encompasses from smart homes to industrial automation, healthcare services to agriculture. IoT is considered one of the milestones for the future thanks to its promising potential and many application areas. However, data protection and device reliability must be considered for the sustainable and secure implementation of IoT. In addition to the innovations introduced by IoT, effectively managing the challenges and minimizing related is critical for this technology to achieve its desired potential. Although IoT enhances efficiency and user experience across various organizations and industries, it also combats serious cyber threats due to potential security vulnerabilities. The thesis aims to conduct a security assessment of IoT devices and to research and develop methods for detecting Distributed Denial of Service (DDoS) attacks on these devices using Random Forest, Logistic Regression, K-Nearest Neighbors (KNN), Neural Network, Navie Bayes, Decision Tree, RBF SVM and QDA models.