TeMIA-NT: ThrEat Monitoring and Intelligent data Analytics of Network Traffic

• Published in: 2020 4th Conference on Cloud and Internet of Things (CIoT) pp. 9 - 16
• Main Authors: Guimaraes, Lucas C. B., Rebello, Gabriel Antonio F., Fernandes, Felipe S., Camilo, Gustavo F., de Souza, Lucas Airam C., dos Santos, Danyel C., de Oliveira, Luiz Gustavo C. M., Duarte, Otto Carlos M. B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.10.2020
• DOI: 10.1109/CIoT50422.2020.9244206

Cybernetic attacks have been increasingly common and cause great harm to people and organizations. Late detection of such attacks increases the possibility of irreparable damage, with high financial losses being a common occurrence. This article proposes TeMIA-NT (ThrEat Monitoring and Intelligent data Analytics of Network Traffic), a real-time flow analysis system that uses parallel flow processing. The main contributions of the TeMIA-NT are: i) the proposal of an architecture for realtime detection of network intrusions that supports high traffic rates, ii) the use of the structured streaming library, and iii) two modes of operation: offline and online. The offline operation mode allows evaluating the performance of multiple machine learning algorithms over a given dataset, including metrics such as accuracy, F1-score, and area under the curve (AUC). The proposal uses dataframe structures, in online mode, the structured streaming library in continuous mode, which allows detection of threats in real-time and a quick reaction to attacks. To prevent or minimize the damage caused by security attacks, TeMIA-NT achieves flow-processing rates that reach 50 GB/S.