###### Abstract
Furthermore, the continuous evolution in attack patterns created by attackers to circumvent detection mechanisms creates ongoing challenges for security professionals. Additionally, the significant growth in legitimate traffic volume is creating additional complexity and challenges in differentiating between legitimate and attack traffic [15]. Studies by [16; 17] show that the increase in IoT device usage has introduced new attack vectors that traditional detection systems struggle to discover effectively. DDoS attacks present a real threat to IoT environments due to their inherent vulnerabilities, including limited memory and processing capacity. The complexity and magnitude of modern DDoS attacks are not adequately addressed by conventional security measures [15]. Cyber threat detection and mitigation may be enhanced by using machine learning (ML) solutions, such as supervised and deep ML algorithms, which can handle enormous datasets, discover complex patterns, and provide adaptive detection [18]. The effectiveness of these ML algorithms in detecting DDoS attacks in IoT systems should be thoroughly investigated. Although earlier studies have looked at ML techniques, they have not focused on the importance of feature selection when it comes to a model's effectiveness. Moreover, most previous efforts used accuracy as the primary focus to show a model's efficacy neglecting factors such as false-positive rates and training/detection time. Taking all of these factors into account provides insight on the optimal detection strategies to employ, given the limited resources and scalability of IoT devices. As a result, the choice of the appropriate ML models for network security in general and DDoS attacks in particular has become crucial in addressing these challenges. Different ML and DL models showed capabilities in handling network traffic characteristics, processing speed, and detection accuracy rates. Recent studies [19; 20] showed that the selection of the model can highly affect the detection accuracy rate up to approximately 25% and the processing speed by up to 69-76% under similar conditions in various domains. Furthermore, feature selection techniques effectively minimize training time and enhance the accuracy of ML-based detection [20, 21]. The efficacy of different ML models varies noticeably, with some models showing superior performance in detecting certain attack types while struggling with other types. Using the Edge-IIoTset dataset [22], this paper aims to thoroughly evaluate the effectiveness of different supervised ML classification algorithms to enhance detection of DDoS attacks in IoT environments.The most accurate and efficient models for attack detection is recommended by analyzing the computational training and detection time in addition to the performance metrics comparison. The research methodology employs a systematic comparative analysis using the Edge-IIoTset dataset to assess multiple ML classification algorithms. The experimental framework covers data pre-processing, feature selection, and model training phases. The evaluated classification models are: LSTM, RF, CNN, KNN with varying k values (3, 5, 7), and SVM. Performance evaluation is carried out through statistical analysis of standard metrics including confusion matrix, while training time and detection speed are measured to evaluate computational efficiency. The primary contributions of this research include:  
* Presenting a detailed comparison of supervised ML and DL techniques in DDoS attack detection on IoT devices using the Edge-IIoTset dataset. Designed for IoT and Industrial Internet of Things (IIoT), this dataset provides a realistic and comprehensive cybersecurity resource. * Investigating the important effect of feature selection on the accuracy and efficiency of detection models, an area that previous studies have not sufficiently emphasized. * Evaluating several useful performance indicators, such as false-positive rates, training time, detection accuracy, and detection speed.