Banca de DEFESA: Danilo Anderson de Moura Chagas

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : Danilo Anderson de Moura Chagas
DATE: 30/01/2024
TIME: 10:00
LOCAL: https://teams.microsoft.com/l/meetup-join/19%3a0a9998084fcc459f92b72fb6608767f7%40thread.tacv2/17046
TITLE:

Detection of Denial-of-Service Attacks in DBMSs from Internal Logs Using Supervised and Unsupervised Approaches

KEY WORDS:

Denial-of-Service; DBMS; Supervised ML; Unsupervised ML

PAGES: 66
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

Denial-of-Service (DoS) attacks impose threats to the accomplishment of an organization’s purposes once they result in serious issues related to the availability of information systems. DoS attacks have been extensively studied in the literature, especially in their most dangerous form, the Distributed Denialof-Service (DDoS). However, existing works usually focus on the network and transport layers or protocols like HTTP. Database, a critical infrastructure for service provision, has mechanisms for recording information (logs) of SQL queries and sessions, which generates large volumes of data. Although databases are vulnerable to DDoS, they are not entirely covered by commercial tools or research on detecting such attacks. Machine Learning (ML) techniques are highly effective in identifying patterns in large amounts of data, such as database SQL logs. Thus, this work developed the application of ML to detect DDoS attacks on a database from the logs of queries executed on it. It makes use of two complimentary approaches of ML: supervised and unsupervised. As a result, the classification of records obtained an F1-score of 94.44\% and the Anomaly Detection achieved an F1- score of 75.75\%, which indicates the effectiveness of the developed approaches.

COMMITTEE MEMBERS:
Interno - 3085005 - GERALDO PEREIRA ROCHA FILHO
Externo à Instituição - JOSE RODRIGUES TORRES NETO - UFPI
Interno - 2363646 - RAFAEL RABELO NUNES
Interno - 1415757 - VINICIUS PEREIRA GONCALVES