@article{M4AD4C920,
title = "Attention Based Collaborative Source-Side DDoS Attack Detection",
journal = "The Transactions of the Korea Information Processing Society",
year = "2024",
issn = "null",
doi = "https://doi.org/10.3745/TKIPS.2024.13.4.157",
author = "Hwisoo Kim, Songheon Jeong, Kyungbaek Kim",
keywords = "DDos, Collaborative Source-Side Attack Detection, Attention",
abstract = "The evolution of the Distributed Denial of Service Attack(DDoS Attack) method has increased the difficulty in the detection process. 
One of the solutions to overcome the problems caused by the limitations of the existing victim-side detection method was the source-side 
detection technique. However, there was a problem of performance degradation due to network traffic irregularities. In order to solve 
this problem, research has been conducted to detect attacks using a collaborative network between several nodes based on artificial 
intelligence. Existing methods have shown limitations, especially in nonlinear traffic environments with high Burstness and jitter. To 
overcome this problem, this paper presents a collaborative source-side DDoS attack detection technique introduced with an attention 
mechanism. The proposed method aggregates detection results from multiple sources and assigns weights to each region, and through 
this, it is possible to effectively detect overall attacks and attacks in specific few areas. In particular, it shows a high detection rate 
with a low false positive of about 6% and a high detection rate of up to 4.3% in a nonlinear traffic dataset, and it can also confirm 
improvement in attack detection problems in a small number of regions compared to methods that showed limitations in the existing 
nonlinear traffic environment."
}