###### Abstract  
As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-ItoSet dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time. artificial intelligence; machine learning; DDoS; deep learning; network security; detection; IoT security +
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essential web services and cause serious operational and financial harm. In the financial sector, DDoS attacks have the potential to cause significant operational delays and financial losses [7]. These attacks can result in reputation damage in addition to the financial losses, especially for banks. The effects are not limited to the main target in the attack; they also encompass shared infrastructure, cloud providers, and energy usage [8]. The rapid increase in IoT devices raises cybersecurity risks in many areas and particularly for financial institutions, making them more exposed to attacks [9]. Devices such as cameras, sensors, and door controls are vulnerable, as attackers after compromising these devices could interrupt daily operations or gain access to sensitive information [10]. The IoT consists of interconnected devices that are capable of sharing data over the internet. The IoT network is undergoing an exponential growth with an increase from 30 billion devices in 2021 to an expectation of more than 75 billion in 2025 [11]. IoT devices vary widely in design and purpose, encompassing everything from smart household appliances and thermostats to industrial sensors and autonomous vehicles. Although the interconnectivity between IoT devices enhances data gathering and system operations, it introduces significant security vulnerabilities. Of particular concern is DDoS attacks, where attackers exploit vulnerable IoT devices to create large botnets capable of overwhelming network resources and disrupting major and critical services [1; 12]. These disruptions could have an impact on many services, particularly in the health and financial sectors. Within the health sector, these attacks could disrupt critical operations, impact patient monitoring and decision-making in healthcare IoT environments [13]. Similarly, any interruption of services in the financial sector leads to a negative effect on the stock market and potential losses of thousands of dollars for every hour of downtime [14]. The detection and classification of DDoS attacks face several complex challenges in modern network environments. The effectiveness of the traditional signature-based detection methods has been reduced due to the adoption of traffic encryption [15].