Ανάλυση και συσχέτιση αζήτητης ηλεκτρονικής επικοινωνίας
Spam analysis and correlation engine
KeywordsKibana ; Logstash ; ElasticSearch ; JWT ; Celery ; ELK ; MongoDB ; e-mails ; Spam (Electronic mail) ; Malicious software ; API ; Analysis ; Dashboard ; Python ; Databases ; MySQL ; Mails
This dissertation focuses on the creation of an application that collects e-mail headers in order to analyze them and find conditions from which an e-mail may be labeled as spam. The application was implemented in python language. The e-mail headers are sent to the individual services using the JSON Web Token and when sent for analyzing purposes they are placed in Celery Task Queue. The data was stored in MySQL, MongoDB database and ElasticSearch. Additional data configuration was performed with the help of Logstash and Kibana was used for data visualization capabilities.