LOW-SPEED HTTP DDOS ATTACK PREVENTION MODEL FOR END USERS

Slow HTTP DDoS attacks pose a serious threat to information systems and web services because they use sophisticated techniques to exhaust server resources. These attacks specifically target compute resource exhaustion, request throughput, or connection management at the application layer [1]. Biased modeling of such attacks requires a special approach to analyze traffic behavior and request characteristics, allowing anomalies to be detected even with minimal network activity [2], [15]. The main problem with such attacks is that they are difficult to recognize because of their similarity to legitimate traffic. Therefore, it is necessary to develop intelligent systems that can analyze the complex interaction patterns between clients and servers. The proposed model is based on a complex analysis of network activity using a layered threat detection system. The model utilizes machine learning algorithms that adapt to changing attack characteristics and improve the accuracy of detecting subtle anomalies in traffic [3], [13]. This minimizes the number of false positives and allows the system to respond quickly to changes in the attack vector. Simulation results demonstrate the effectivene