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Anomaly Detection in the Service Mes...

Fosdick, Brent.

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Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions./
Author:	Fosdick, Brent.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	26 p.
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792440
ISBN:	9780438044869

Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions.
Fosdick, Brent.

Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 26 p.

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--University of Colorado at Boulder, 2018.

Microservice architectures enable the continuous delivery and deployment of complex distributed systems. With this architecture choice comes the challenge of connecting, managing, and securing communications between the various microservices. Recent application level denial of service attacks which target backend services have demonstrated a rising attack vector that is capable of bringing down enterprise level systems with minimal cost and effort. By targeting expensive services using readily available cloud computing services, a single user is capable of backing up an entire system and causing widespread outages.

ISBN: 9780438044869Subjects--Topical Terms:

523869
Computer science.

Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions.
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245 1 0 $a Anomaly Detection in the Service Mesh for Making Policy Enforcement Decisions.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 26 p.
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Joe McManus.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2018.
520 $a Microservice architectures enable the continuous delivery and deployment of complex distributed systems. With this architecture choice comes the challenge of connecting, managing, and securing communications between the various microservices. Recent application level denial of service attacks which target backend services have demonstrated a rising attack vector that is capable of bringing down enterprise level systems with minimal cost and effort. By targeting expensive services using readily available cloud computing services, a single user is capable of backing up an entire system and causing widespread outages.
520 $a This paper seeks to research and better understand the feasibility of using machine learning models to analyze traffic between microservices in order to perform anomaly detection in the service mesh. The goal of this research is to utilize these models to perform real time policy enforcement decisions based on changes in requests between services.
590 $a School code: 0051.
650 4 $a Computer science. $3 523869
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792440