A Machine-Synesthetic Approach To DDoS Network Attack Detection
This addresses the need for detecting unknown (zero-day) attacks in network security, though it appears incremental as it adapts existing image classification methods to a new domain.
The authors tackled the problem of detecting DDoS network attacks by proposing a machine-synesthetic approach that projects network traffic data into images, allowing the use of image classification algorithms for anomaly detection, achieving a complex efficiency indicator of 97% on a large sample.
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%.