DDoS attacks, which aim to overwhelm a system with requests, are commonplace in the cyber world. In this type of assault, bandwidth and processing resources are deliberately clogged in order to disrupt the interactions of legitimate users. These attacks operate by inundating the victim's system with a deluge of packets, rendering it inaccessible. Diverging from the singular source of Denial of Service (DoS) attacks, DDoS attacks emanate from a multitude of servers, magnifying their impact. Over the last decade, a concentrated effort has been invested in comprehending the orchestration and authentication of DDoS attacks, resulting in valuable insights into discerning attack patterns and suspicious activities. Currently, the focus has shifted towards real-time detection within the stream of network transactions, constituting a critical research domain. Yet, this focus often sidelines the importance of benchmarking DDoS attack assertions within the streaming data framework. As a remedy, the Anomaly-based Real-Time Prevention (ARTP) framework has been formulated, designed specifically to combat application layer DDoS attacks, particularly targeting web applications. Employing advanced machine learning techniques, ARTP offers adaptable methodologies to swiftly and accurately pinpoint application-layer DDoS attacks. Rigorous testing on a representative LLDoS (Low Level DoS) benchmark dataset has affirmed the resilience and efficiency of the proposed ARTP model, underscoring its capacity to achieve the research objectives set forth.