# A Machine-Synesthetic Approach To DDoS Network Attack Detection

Source: [A Machine-Synesthetic Approach To DDoS Network Attack Detection](<https://www.sota2.com/research/paper/68fa65811e209131cea0a9bf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:28:43.855Z

Authors: Yuri Monakhov, Oleg Nikitin, Anna Kuznetsova, Alexey Kharlamov, Alexandr Amochkin

Published: 2019-01-13T17:01:46.000Z

## 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%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.04017>)

[PDF](<https://arxiv.org/pdf/1901.04017.pdf>)