Zero-day DDoS Attack Detection

TL;DR

This paper presents a neural network-based method for detecting zero-day DDoS attacks by analyzing pre-entry network traffic with advanced feature extraction techniques.

Contribution

It introduces a novel approach combining modern feature extraction with neural networks to identify unseen DDoS threats before they reach private networks.

Findings

Effective detection of zero-day DDoS attacks demonstrated

Utilizes pre-entry network traffic for early threat identification

Combines feature extraction with neural network classification

Abstract

The ability to detect zero-day (novel) attacks has become essential in the network security industry. Due to ever evolving attack signatures, existing network intrusion detection systems often fail to detect these threats. This project aims to solve the task of detecting zero-day DDoS (distributed denial-of-service) attacks by utilizing network traffic that is captured before entering a private network. Modern feature extraction techniques are used in conjunction with neural networks to determine if a network packet is either benign or malicious.