DDoS Attack Detection by Hybrid Deep Learning Methodologies

A Distributed Denial of Service (DDoS) attack occurs when large amounts traffic from hundreds, thousands, or even millions other computers are routed to a network server crash the system and disrupt its function. These attacks commonly used shut down websites applications temporarily. Such problems often need be addressed with models that can manage time information contained in flows. In this work, we apply Hybrid Deep Learning method detect malicious web form DDoS attacks, controlling flow reaching server, using any dependencies between different elements data stream. An original cutting-edge Hierarchical Temporal Memory (HTM) hybrid model has been proposed. The operation is predicated primarily on portion cerebral cortex known as neocortex. neocortex charge various fundamental brain functions, including perception senses, comprehension language, control movement. For implementation capable encoding sequences incorporate incoming data, Long Short-Term (LSTM) shell added.