Nidisha Joshy K J (92216017) <br/><br/>
FIRE OPS (Anomaly detection and correlation) 

 - MSC DA 2016-2018 
<br/><br/> “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/>  
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<br/><br/><label>Subjects--Topical Terms: </label><br/>FIRE OPS<br/>ELK STACK<br/>MACHINE LEARNING