DDoS Attack Detection by Hybrid Deep Learning Methodologies

Abstract

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