The escalating frequency and complexity of Distributed Denial of Service (DDoS) attacks present formidable obstacles to maintaining network security. As these threats become more sophisticated, conventional detection methodologies often prove inadequate in both recognizing and countering them, primarily due to their static nature and inability to adapt to the dynamic evolution of attack strategies. This thesis delves into the deployment of a spectrum of deep learning techniques for the detection of DDoS attacks, with the objective of bolstering the resilience and precision of existing detection frameworks. By harnessing the power of known labeled datasets, the study empowers advanced deep learning algorithms to discern complex patterns and behaviors that are indicative of DDoS attacks. These algorithms are trained to identify subtle nuances and anomalies that may elude traditional detection systems. The research further appraises the efficacy of these deep learning models by subjecting them to a dataset generated from real-world traffic, thereby offering an exhaustive evaluation of their operational performance in authentic scenarios. Moreover, the study incorporates a multifaceted approach to experimentation and analysis, meticulously examining various deep learning architectures and configurations to ascertain the most efficacious models for DDoS detection. This comprehensive exploration includes the assessment of convolutional neural networks (CNNs), deep neural networks (DNNs), and other advanced architectures each offering unique strengths in pattern recognition and anomaly detection. In addition to model evaluation, the thesis proposes a novel framework for continuous learning, where the models are periodically updated with new data, allowing them to evolve in tandem with the ever-changing landscape of cyber threats. This adaptive mechanism ensures that the detection system remains effective against both current and emerging DDoS tactics. Furthermore, the research contemplates the integration of these deep learning models into existing network infrastructure, discussing the practical considerations and potential challenges of implementation, such as computational demands, real-time processing requirements, and the need for extensive training data. This thesis makes significant contributions to the field of network security by identifying the limitations of traditional DDoS detection methods and proposing advanced deep learning techniques as robust alternatives. It provides a comprehensive evaluation of various deep learning models trained on labeled datasets and tested on real-world data, demonstrating their effectiveness in recognizing complex attack patterns. Additionally, the research introduces a novel framework for continuous learning, ensuring the adaptability of detection systems to evolving threats. The practical integration of these models into existing network infrastructure is also addressed, offering a holistic approach to enhancing DDoS detection and paving the way for future advancements in cybersecurity.