On Machine Learning DoS Attack Identification from Cloud Computing Telemetry

Authors: João Henrique Corrêa, Patrick Marques Ciarelli, Moises R. N. Ribeiro, Rodolfo da Silva Villaca
Published: 2019-04-11

Source: https://arxiv.org/abs/1904.06211

PDF: https://arxiv.org/pdf/1904.06211

AIにより推定されたラベル

機械学習手法 DDoS攻撃 データ収集

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

The detection of Denial of Service (DoS) attacks remains a challenge for the cloud environment, affecting a massive number of services and applications hosted by such virtualized infrastructures. Typically, in the literature, the detection of DoS attacks is performed solely by analyzing the traffic of packets in the network. This work advocates for the use of telemetry from the cloud to detect DoS attacks using Machine Learning algorithms. Our hypothesis is based on richness of such native data collection services, with metrics from both physical and virtual hosts. Our preliminary results demonstrate that DoS can be identified accurately with k-Nearest Neighbors (kNN) and decision tree (CART).