On Machine Learning DoS Attack Identification from Cloud Computing Telemetry

April 11, 2019 · Entered Twilight · 🏛 arXiv.org

"No code URL or promise found in abstract"
"Code repo scraped from project page (backfill)"

Evidence collected by the PWNC Scanner

Repo contents: ACM-Reference-Format.bst, Makefile, README.md, acmart.cls, body.tex, figures, paper.tex, reference.bib

Authors João Henrique Corrêa, Patrick Marques Ciarelli, Moises R. N. Ribeiro, Rodolfo da Silva Villaca arXiv ID 1904.06211 Category cs.CR: Cryptography & Security Cross-listed cs.LG, cs.NI, stat.ML Citations 2 Venue arXiv.org Repository https://github.com/scyue/ccp-sigcomm18 ⭐ 73 Last Checked 29 days ago

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).