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master's thesis
Anomaly detection in AWS load balancer logs
Split: University of Split, Faculty of Science, 2023. urn:nbn:hr:166:998425
Jankuleski, Marko
University of Split
Faculty of Science
Department of Informatics

Institutional repository: Repository of Faculty of Science

Cite this document

Jankuleski, M. (2023). Anomaly detection in AWS load balancer logs (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:998425

Jankuleski, Marko. "Anomaly detection in AWS load balancer logs." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:998425

Jankuleski, Marko. "Anomaly detection in AWS load balancer logs." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:998425

Jankuleski, M. (2023). 'Anomaly detection in AWS load balancer logs', Master's thesis, University of Split, Faculty of Science, accessed 16 July 2024, https://urn.nsk.hr/urn:nbn:hr:166:998425

Jankuleski M. Anomaly detection in AWS load balancer logs [Master's thesis]. Split: University of Split, Faculty of Science; 2023 [cited 2024 July 16] Available at: https://urn.nsk.hr/urn:nbn:hr:166:998425

M. Jankuleski, "Anomaly detection in AWS load balancer logs", Master's thesis, University of Split, Faculty of Science, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:166:998425

Metadata
TitleAnomaly detection in AWS load balancer logs
Title (croatian)Detekcija anomalija u logovima AWS balansera opterećenja
AuthorMarko Jankuleski
MentorIvo Ugrina (mentor)
Committee memberIvo Ugrina (predsjednik povjerenstva)
Committee memberMilica Klaričić Bakula (član povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2023-10-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Amazon Web Services (AWS) is a widely used cloud platform. Load Balancing service that it uses is the Elastic Load Balancing service. Among its load-balancing options, the Application Load Balancer (ALB) is crucial for businesses as it routes incoming requests to one or multiple targets. In this thesis we aimed to explore the current scope of research on anomaly detection in ALB logs and have concluded that there is limited research evaluating ALB’s anomaly detection in log files, and that no benchmark dataset for AWS ALB research is widely available. Additionally, we wanted to look at some computational approaches with logs and anomaly detection and have thus assembled a set of log files from an AWS Application Load Balancer from a private company and have touched upon various anomaly detection methods applied to these logs considering that we have not had enough time to label the full data we have collected. In our work, we have been guided by a systematic review of literature in the fields of time series anomaly detection and log file anomaly detection.
Abstract (croatian)
Amazon Web Services (AWS) široko je korištena platforma u oblaku. Za balansiranje opterećenja koristi Elastic Load Balancing. Među opcijama za balansiranje opterećenja, Application Load Balancer ključan je za poslovanje tvrtki jer upravlja dolaznim zahtjevima na način da ih prosljeđuje jednom ili vise ciljeva. Nismo pronašli znanstvene radove koji se bave detekcijom anomalija koristeći Amazon CloudWatch sustav za detekciju anomalija i detekcijom anomalija u logovima AWS balansera opterećenja te nismo pronašli skup podatka za AWS koji bi se koristio kao referentni skup podataka. Kako bi to riješili, u okviru istraživanja smo prikupili logove AWS Application Load Balancera i procijenili smo različite metode detekcije anomalija, vođeni sustavnim pregledom istraženosti područja detekcije anomalija u vremenskim nizovima i detekcije anomalija u logovima balansera opterećenja.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:166:998425
Study programmeTitle: Data Science and Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra podatkovne znanosti i inženjerstva (magistar/magistra podatkovne znanosti i inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-11-24 12:02:11