# A Machine-Synesthetic Approach To DDoS Network Attack Detection

## TL;DR

This paper introduces a novel anomaly detection method for DDoS attacks using machine synesthesia, transforming network traffic data into images to leverage image classification algorithms, achieving high detection accuracy.

## Contribution

It presents a new approach that applies image classification techniques to network anomaly detection by projecting traffic data into images, enabling the use of advanced image detection methods.

## Key findings

- Detection accuracy reaches 97% on large samples.
- The method effectively detects unknown zero-day attacks.
- High efficiency in anomaly detection demonstrated.

## Abstract

In the authors' opinion, anomaly detection systems, or ADS, seem to be the most perspective direction in the subject of attack detection, because these systems can detect, among others, the unknown (zero-day) attacks. To detect anomalies, the authors propose to use machine synesthesia. In this case, machine synesthesia is understood as an interface that allows using image classification algorithms in the problem of detecting network anomalies, making it possible to use non-specialized image detection methods that have recently been widely and actively developed. The proposed approach is that the network traffic data is "projected" into the image. It can be seen from the experimental results that the proposed method for detecting anomalies shows high results in the detection of attacks. On a large sample, the value of the complex efficiency indicator reaches 97%.