Earticle

초록

한국어

Low-Rate Denial of Service (LDoS) attacks raise an increasingly frequent and significant threat to performancecritical and sensitive networks. Due to their slowly evolving nature, it is challenging –but crucial– to detect such attacks during their early phases in order to mitigate their impact on network performance, e.g. Quality of Service (QoS), in longterm operation. To this end, this paper investigates the prediction of QoS via a modified version of the Recurrent Trend Predictive Neural Network (rTPNN) and the use of the prediction towards detracting LDoS attacks. The presented rTPNN-based QoS predictor is evaluated and compared against benchmark models for five scenarios using an open-access dataset. The results have shown that the modified rTPNN model can predict QoS with under 2% SMAPE, and the QoS prediction is a promising approach for developing LDoS attack detectors in future works.

목차

Abstract
I. INTRODUCTION
II. RECURRENT TREND PREDICTIVE NEURAL NETWORK FOR QOS PREDICTION
A. Traffic Flow Features
B. Modified rTPNN
III. RESULTS
A. Dataset
B. QoS Prediction on Normal Traffic Data
C. QoS Prediction on Normal Traffic Data
IV. CONCLUSION
REFERENCES

키워드

저자

• Mert Nakıp [ Institute of Theoretical and Applied Informatics Polish Academy of Sciences, IITIS-PAN 44-100 Gliwice, Poland ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004