Implementing Chaos Based Optimisations on Neural Networks for Predictions of Distributed Denial-of-Service (DDoS) Attacks

Abstract

A Distributed Denial-of-Service attack (DDoS) involves overwhelming a network with a large amount of traffic that aims to disrupt the normal functioning of a network. DDoS attacks can cause a variety of problems, such as website downtime, loss of revenue, and damage to a company’s reputation. One of the main challenges in dealing with DDoS attacks is detecting them in a timely and accurate manner.Machine learning algorithms can be trained to recognize patterns in network traffic that are indicative of a DDoS attack, and they can also be used to distinguish between legitimate traffic and attack traffic. The paper discusses a method for improving the performance of neural networks by utilizing chaos-based algorithms for detection and prediction of DDoS attacks. Moreover, this paper talks about using chaos-based optimization to speed up the process of training a model using neural networks. This technique uses a chaotic sequence to set the initial weights and biases of the model, which can result in a wider range of random starting points. This can help the model to find the best solution faster.