FIRE OPS (Anomaly detection and correlation) (Record no. 6057)

MARC details
000 -LEADER
fixed length control field 02074nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122838.0
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fixed length control field 180524b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Nidisha Joshy K J (92216017)
9 (RLIN) 14040
245 ## - TITLE STATEMENT
Title FIRE OPS (Anomaly detection and correlation)
300 ## - PHYSICAL DESCRIPTION
Extent MSC DA 2016-2018
500 ## - GENERAL NOTE
General note “Fire Ops” is an internal platform of UST Global for the anomaly detection and resolution internal<br/>IT infrastructure. Fire Ops is a 3-stage project in which aims at the complete automation in the<br/>field of anomaly detection. Fire Ops aims at making a self-learning anomaly detection system in<br/>involving the process of “Unsupervised Learning” in the Machine Learning and their algorithms<br/>to learn and understand the pattern of the CPU utilization and memory utilization. The first stage<br/>of this project is to detect anomaly from different types of logs such as system logs, IOT logs,<br/>server log, application log etc. In this project we use ELK stack for anomaly detection. ELK stack<br/>is the acronym for three open source projects: Elasticsearch, Logstash, Kibana. In our project<br/>elasticsearch act as database, Logstash is a platform used for log parsing with the help of grok<br/>debugger, Kibana lets users visualize data with charts and graphs in elasticsearch. When an<br/>anomaly is detected an alert is send to an email id with the help of a watcher. The second stage of<br/>this project is to find the root cause of each anomaly. For that, here we used some of the modules:<br/>Auto discovery, CMDB, Dependency Mapping, Correlation and Root cause analysis. In this<br/>project, we are using adaptive threshold method because it learns automatically. This project is<br/>useful for it infrastructure, healthcare, business etc. The future scope of our project is to predict<br/>and forecast the future patterns, availability and performance of an infrastructure. <br/> <br/>
502 ## - DISSERTATION NOTE
Degree type MSC DA
Name of granting institution 2016-2018
Year degree granted INT
-- Dr. Manoj Kumar T K
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element FIRE OPS
9 (RLIN) 14041
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ELK STACK
9 (RLIN) 14042
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 14043
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   24/05/2018   R-1364 24/05/2018 24/05/2018 Project Reports