In the field of cybersecurity, Distributed Denial of Service attacks increased in significance. These attacks can cause short-term or long-term service disruptions for system participants. Digital applications, financial institutions, and e-commerce platforms are frequently targeted. Due to the extensive impact by these attacks, their detection is crucial. They may be recognized using supervised machine learning techniques. This work applies Support Vector Machine which belongs to the family of supervised learning methods, to recognize distributed Denial of Service attacks. To ensure efficient management and accessibility, this approach stores network traffic data in SQLite3. Experimental result show that the support vector machine is successful in differentiating between normal and abnormal traffic. To improve the accuracy and reliability of the detection model, the collected data is pre-processed by handling missing values and applying feature scaling using Standard Scaler. In this paper, we present novel methods that outperform existing techniques for launching and mitigating distributed denial of service attacks. To provide a comprehensive understanding of the problem, we also classify different methods for initiating and detecting these attacks. Furthermore, we compare our attack module with some existing tools.