Metaheuristic-Driven Feature Selection with SVM and KNN for Robust DDoS Attack Detection: A Comparative Study

Distributed Denial of Service (DDoS) attack occurs when an attacker attempts to disrupt the normal operation of a network, service, or website by overwhelming it with a high volume of internet traffic. The goal of detecting DDoS attacks is to identify and respond to them promptly, thereby minimizing their impact on the targeted system. Effective detection is essential for individuals, organizations, and network administrators to safeguard infrastructure, ensure service availability, and protect online systems and services. DDoS detection is widely applicable in areas such as network security, web service protection, cloud computing, and online infrastructure resilience. To address this need, we propose a framework consisting of six main steps. First, data collection involves gathering network traffic information, system activity logs, and known instances of DDoS attacks. Second, relevant features are identified from the dataset, including traffic patterns, packet sizes, IP addresses, and protocol types. In the third step, feature selection is performed using metaheuristic algorithms such as the Salp Swarm Algorithm (SSA), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) to isolate the most informative features for distinguishing between normal and malicious traffic. Fourth, the dataset is divided into training and testing subsets for model development and evaluation. Fifth, classification models are built using machine learning algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to classify traffic patterns effectively. Finally, the performance of the models is evaluated using metrics including accuracy, precision, recall, and F1-score. The results of the proposed framework demonstrate outstanding performance, with classification accuracy reaching up to 99.9%. In summary, detecting DDoS attacks is vital for protecting networked systems and ensuring the continuity of online services, and the use of feature selection and machine learning techniques significantly enhances detection accuracy and efficiency.