Zero-day DDoS Attack Detection
This addresses the issue of evolving attack signatures for network security professionals, though it appears incremental as it builds on existing neural network methods.
The paper tackles the problem of detecting zero-day DDoS attacks in network security by using network traffic captured before entering a private network, achieving detection through modern feature extraction and neural networks, but no concrete numbers are provided.
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.